@InProceedings{lipp2021neologism_demo,
author="Lipp, Johannes
and Gleim, Lars
and Cochez, Michael
and Dimitriadis, Iraklis
and Ali, Hussain
and Alvarez, Daniel Hoppe
and Lange, Christoph
and Decker, Stefan",
editor="Verborgh, Ruben
and Dimou, Anastasia
and Hogan, Aidan
and d'Amato, Claudia
and Tiddi, Ilaria
and Br{\"o}ring, Arne
and Mayer, Simon
and Ongenae, Femke
and Tommasini, Riccardo
and Alam, Mehwish",
title="Towards Easy Vocabulary Drafts with Neologism 2.0",
booktitle="The Semantic Web: ESWC 2021 Satellite Events",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="21--26",
url="https://openreview.net/forum?id=PoAI5RrlUFj",
abstract="Shared vocabularies and ontologies are essential for many applications. Although standards and recommendations already cover many areas, adaptations are usually necessary to represent concrete use-cases properly. Domain experts are unfamiliar with ontology engineering, which creates special requirements for needed tool support. Simple sketch applications are usually too imprecise, while comprehensive ontology editors are often too complicated for non-experts. We present Neologism 2.0 -- an open-source tool for quick vocabulary creation through domain experts. Its guided vocabulary creation and its collaborative graph editor enable the quick creation of proper vocabularies, even for non-experts, and dramatically reduces the time and effort to draft vocabularies collaboratively. An RDF export allows quick bootstrapping of any other Semantic Web tool.",
isbn="978-3-030-80418-3"
}

Shared vocabularies and ontologies are essential for many applications. Although standards and recommendations already cover many areas, adaptations are usually necessary to represent concrete use-cases properly. Domain experts are unfamiliar with ontology engineering, which creates special requirements for needed tool support. Simple sketch applications are usually too imprecise, while comprehensive ontology editors are often too complicated for non-experts. We present Neologism 2.0 – an open-source tool for quick vocabulary creation through domain experts. Its guided vocabulary creation and its collaborative graph editor enable the quick creation of proper vocabularies, even for non-experts, and dramatically reduces the time and effort to draft vocabularies collaboratively. An RDF export allows quick bootstrapping of any other Semantic Web tool.

@article{hogan2021knowledge,
author = {Hogan, Aidan and Blomqvist, Eva and Cochez, Michael and D’amato, Claudia and Melo, Gerard De and Gutierrez, Claudio and Kirrane, Sabrina and Gayo, Jos\'{e} Emilio Labra and Navigli, Roberto and Neumaier, Sebastian and Ngomo, Axel-Cyrille Ngonga and Polleres, Axel and Rashid, Sabbir M. and Rula, Anisa and Schmelzeisen, Lukas and Sequeda, Juan and Staab, Steffen and Zimmermann, Antoine},
title = {Knowledge Graphs},
year = {2021},
issue_date = {July 2021},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {54},
number = {4},
issn = {0360-0300},
url = {https://arxiv.org/abs/2003.02320},
doi = {10.1145/3447772},
abstract = {In this article, we provide a comprehensive introduction to knowledge graphs, which
have recently garnered significant attention from both industry and academia in scenarios
that require exploiting diverse, dynamic, large-scale collections of data. After some
opening remarks, we motivate and contrast various graph-based data models, as well
as languages used to query and validate knowledge graphs. We explain how knowledge
can be represented and extracted using a combination of deductive and inductive techniques.
We conclude with high-level future research directions for knowledge graphs.},
journal = {ACM Comput. Surv.},
month = jul,
articleno = {71},
numpages = {37},
keywords = {ontologies, shapes, graph query languages, rule mining, graph algorithms, embeddings, graph neural networks, graph databases, Knowledge graphs}
}

In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.

@InProceedings{chakrabarti2021feature-selection,
author="Chakrabarti, Arnab
and Das, Abhijeet
and Cochez, Michael
and Quix, Christoph",
editor="Bellatreche, Ladjel
and Dumas, Marlon
and Karras, Panagiotis
and Matulevi{\v{c}}ius, Raimundas",
title="Unsupervised Feature Selection for Efficient Exploration of High Dimensional Data",
booktitle="Advances in Databases and Information Systems",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="183--197",
url={https://www.cochez.nl/papers/feature_selection_for_exploration.pdf},
abstract="The exponential growth in the ability to generate, capture, and store high dimensional data has driven sophisticated machine learning applications. However, high dimensionality often poses a challenge for analysts to effectively identify and extract relevant features from datasets. Though many feature selection methods have shown good results in supervised learning, the major challenge lies in the area of unsupervised feature selection. For example, in the domain of data visualization, high-dimensional data is difficult to visualize and interpret due to the limitations of the screen, resulting in visual clutter. Visualizations are more interpretable when visualized in a low dimensional feature space. To mitigate these challenges, we present an approach to perform unsupervised feature clustering and selection using our novel graph clustering algorithm based on Clique-Cover Theory. We implemented our approach in an interactive data exploration tool which facilitates the exploration of relationships between features and generates interpretable visualizations.",
isbn="978-3-030-82472-3"
}

The exponential growth in the ability to generate, capture, and store high dimensional data has driven sophisticated machine learning applications. However, high dimensionality often poses a challenge for analysts to effectively identify and extract relevant features from datasets. Though many feature selection methods have shown good results in supervised learning, the major challenge lies in the area of unsupervised feature selection. For example, in the domain of data visualization, high-dimensional data is difficult to visualize and interpret due to the limitations of the screen, resulting in visual clutter. Visualizations are more interpretable when visualized in a low dimensional feature space. To mitigate these challenges, we present an approach to perform unsupervised feature clustering and selection using our novel graph clustering algorithm based on Clique-Cover Theory. We implemented our approach in an interactive data exploration tool which facilitates the exploration of relationships between features and generates interpretable visualizations.

@ARTICLE{karim2021DeepKneeExplainer,
  author={Karim, Md. Rezaul and Jiao, Jiao and Döhmen, Till and Cochez, Michael and Beyan, Oya and Rebholz-Schuhmann, Dietrich and Decker, Stefan},
  journal={IEEE Access},
  title={DeepKneeExplainer: Explainable Knee Osteoarthritis Diagnosis From Radiographs and Magnetic Resonance Imaging},
  year={2021},
  volume={9},
  number={},
  pages={39757-39780},
  doi={10.1109/ACCESS.2021.3062493}}

@article{alivanistos2021hrqe,
  author    = {Dimitrios Alivanistos and
               Max Berrendorf and
               Michael Cochez and
               Mikhail Galkin},
  title     = {Query Embedding on Hyper-relational Knowledge Graphs},
  journal   = {CoRR},
  volume    = {abs/2106.08166},
  year      = {2021},
  url       = {https://arxiv.org/abs/2106.08166},
  archivePrefix = {arXiv},
  eprint    = {2106.08166},
  timestamp = {Tue, 29 Jun 2021 16:55:04 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2106-08166.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/apin/BekkumBHMT21,
  author    = {Michael van Bekkum and
               Maaike de Boer and
               Frank van Harmelen and
               Andr{\'{e}} Meyer{-}Vitali and
               Annette ten Teije},
  title     = {Modular design patterns for hybrid learning and reasoning systems},
  journal   = {Appl. Intell.},
  volume    = {51},
  number    = {9},
  pages     = {6528--6546},
  year      = {2021},
  url       = {https://doi.org/10.1007/s10489-021-02394-3},
  doi       = {10.1007/s10489-021-02394-3},
  timestamp = {Wed, 01 Sep 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/apin/BekkumBHMT21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/semweb/IdrissouHB21,
  author    = {Al Koudous Idrissou and
               Frank van Harmelen and
               Peter van den Besselaar},
  title     = {Network metrics for assessing the quality of entity resolution between
               multiple datasets},
  journal   = {Semantic Web},
  volume    = {12},
  number    = {1},
  pages     = {21--40},
  year      = {2021},
  url       = {https://doi.org/10.3233/SW-200410},
  doi       = {10.3233/SW-200410},
  timestamp = {Tue, 29 Dec 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/semweb/IdrissouHB21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/data/WangHH21,
  author    = {Xu Wang and
               Frank van Harmelen and
               Zhisheng Huang},
  editor    = {Christoph Quix and
               Slimane Hammoudi and
               Wil M. P. van der Aalst},
  title     = {Biomedical Dataset Recommendation},
  booktitle = {Proceedings of the 10th International Conference on Data Science,
               Technology and Applications, {DATA} 2021, Online Streaming, July 6-8,
               2021},
  pages     = {192--199},
  publisher = {{SCITEPRESS}},
  year      = {2021},
  url       = {https://doi.org/10.5220/0010521801920199},
  doi       = {10.5220/0010521801920199},
  timestamp = {Wed, 28 Jul 2021 15:47:03 +0200},
  biburl    = {https://dblp.org/rec/conf/data/WangHH21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/esws/WangRBH21,
  author    = {Shuai Wang and
               Joe Raad and
               Peter Bloem and
               Frank van Harmelen},
  editor    = {Ruben Verborgh and
               Katja Hose and
               Heiko Paulheim and
               Pierre{-}Antoine Champin and
               Maria Maleshkova and
               {\'{O}}scar Corcho and
               Petar Ristoski and
               Mehwish Alam},
  title     = {Refining Transitive and Pseudo-Transitive Relations at Web Scale},
  booktitle = {The Semantic Web - 18th International Conference, {ESWC} 2021, Virtual
               Event, June 6-10, 2021, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {12731},
  pages     = {249--264},
  publisher = {Springer},
  year      = {2021},
  url       = {https://doi.org/10.1007/978-3-030-77385-4\_15},
  doi       = {10.1007/978-3-030-77385-4\_15},
  timestamp = {Tue, 22 Jun 2021 14:39:38 +0200},
  biburl    = {https://dblp.org/rec/conf/esws/WangRBH21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/aaaiss/2021make,
  editor    = {Andreas Martin and
               Knut Hinkelmann and
               Hans{-}Georg Fill and
               Aurona Gerber and
               Doug Lenat and
               Reinhard Stolle and
               Frank van Harmelen},
  title     = {Proceedings of the {AAAI} 2021 Spring Symposium on Combining Machine
               Learning and Knowledge Engineering {(AAAI-MAKE} 2021), Stanford University,
               Palo Alto, California, USA, March 22-24, 2021},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2846},
  publisher = {CEUR-WS.org},
  year      = {2021},
  url       = {http://ceur-ws.org/Vol-2846},
  urn       = {urn:nbn:de:0074-2846-4},
  timestamp = {Thu, 22 Apr 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/aaaiss/2021make.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2102-11965,
  author    = {Michael van Bekkum and
               Maaike de Boer and
               Frank van Harmelen and
               Andr{\'{e}} Meyer{-}Vitali and
               Annette ten Teije},
  title     = {Modular Design Patterns for Hybrid Learning and Reasoning Systems:
               a taxonomy, patterns and use cases},
  journal   = {CoRR},
  volume    = {abs/2102.11965},
  year      = {2021},
  url       = {https://arxiv.org/abs/2102.11965},
  archivePrefix = {arXiv},
  eprint    = {2102.11965},
  timestamp = {Tue, 02 Mar 2021 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2102-11965.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/apin/BekkumBHMT21,
  author    = {Michael van Bekkum and
               Maaike de Boer and
               Frank van Harmelen and
               Andr{\'{e}} Meyer{-}Vitali and
               Annette ten Teije},
  title     = {Modular design patterns for hybrid learning and reasoning systems},
  journal   = {Appl. Intell.},
  volume    = {51},
  number    = {9},
  pages     = {6528--6546},
  year      = {2021},
  url       = {https://doi.org/10.1007/s10489-021-02394-3},
  doi       = {10.1007/s10489-021-02394-3},
  timestamp = {Wed, 01 Sep 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/apin/BekkumBHMT21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/artmed/RianoWT21,
  author    = {David Ria{\~{n}}o and
               Szymon Wilk and
               Annette ten Teije},
  title     = {Preface: {AIME} 2019},
  journal   = {Artif. Intell. Medicine},
  volume    = {115},
  pages     = {102058},
  year      = {2021},
  url       = {https://doi.org/10.1016/j.artmed.2021.102058},
  doi       = {10.1016/j.artmed.2021.102058},
  timestamp = {Sun, 25 Jul 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/artmed/RianoWT21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/aaaiss/StijnNTV21,
  author    = {Jip J. van Stijn and
               Mark A. Neerincx and
               Annette ten Teije and
               Steven Vethman},
  editor    = {Andreas Martin and
               Knut Hinkelmann and
               Hans{-}Georg Fill and
               Aurona Gerber and
               Doug Lenat and
               Reinhard Stolle and
               Frank van Harmelen},
  title     = {Team Design Patterns for Moral Decisions in Hybrid Intelligent Systems:
               {A} Case Study of Bias Mitigation},
  booktitle = {Proceedings of the {AAAI} 2021 Spring Symposium on Combining Machine
               Learning and Knowledge Engineering {(AAAI-MAKE} 2021), Stanford University,
               Palo Alto, California, USA, March 22-24, 2021},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2846},
  publisher = {CEUR-WS.org},
  year      = {2021},
  url       = {http://ceur-ws.org/Vol-2846/paper15.pdf},
  timestamp = {Thu, 22 Apr 2021 15:54:03 +0200},
  biburl    = {https://dblp.org/rec/conf/aaaiss/StijnNTV21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2102-11965,
  author    = {Michael van Bekkum and
               Maaike de Boer and
               Frank van Harmelen and
               Andr{\'{e}} Meyer{-}Vitali and
               Annette ten Teije},
  title     = {Modular Design Patterns for Hybrid Learning and Reasoning Systems:
               a taxonomy, patterns and use cases},
  journal   = {CoRR},
  volume    = {abs/2102.11965},
  year      = {2021},
  url       = {https://arxiv.org/abs/2102.11965},
  archivePrefix = {arXiv},
  eprint    = {2102.11965},
  timestamp = {Tue, 02 Mar 2021 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2102-11965.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2104-00428,
  author    = {Emile van Krieken and
               Jakub M. Tomczak and
               Annette ten Teije},
  title     = {Storchastic: {A} Framework for General Stochastic Automatic Differentiation},
  journal   = {CoRR},
  volume    = {abs/2104.00428},
  year      = {2021},
  url       = {https://arxiv.org/abs/2104.00428},
  archivePrefix = {arXiv},
  eprint    = {2104.00428},
  timestamp = {Tue, 13 Apr 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-00428.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{xu2021dr,
title = "Biomedical dataset recommendation",
abstract = "Copyright {\textcopyright} 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reservedDataset search is a special application of information retrieval, which aims to help scientists with finding the datasets they want. Current dataset search engines are query-driven, which implies that the results are limited by the ability of the user to formulate the appropriate query. In this paper we aim to solve this limitation by framing dataset search as a recommendation task: given a dataset by the user, the search engine recommends similar datasets. We solve this dataset recommendation task using a similarity approach. We provide a simple benchmark task to evaluate different approaches for this dataset recommendation task. We also evaluate the recommendation task with several similarity approaches in the biomedical domain. We benchmark 8 different similarity metrics between datasets, including both ontology-based techniques and techniques from machine learning. Our results show that the task of recommending scientific datasets based on meta-data as it occurs in realistic dataset collections is a hard task. None of the ontology-based methods manage to perform well on this task, and are outscored by the majority of the machine-learning methods. Of these ML methods only one of the approaches performs reasonably well, and even then only reaches 70\% accuracy.",
author = "X. Wang and {van Harmelen}, F. and Z. Huang",
year = "2021",
doi = "10.5220/0010521801920199",
language = "English",
pages = "192--199",
editor = "C. Quix and S. Hammoudi and {van der Aalst}, W.",
booktitle = "Proceedings of the 10th International Conference on Data Science, Technology and Applications, DATA 2021",
publisher = "SciTePress",
note = "10th International Conference on Data Science, Technology and Applications, DATA 2021 ; Conference date: 06-07-2021 Through 08-07-2021",
}

Copyright © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reservedDataset search is a special application of information retrieval, which aims to help scientists with finding the datasets they want. Current dataset search engines are query-driven, which implies that the results are limited by the ability of the user to formulate the appropriate query. In this paper we aim to solve this limitation by framing dataset search as a recommendation task: given a dataset by the user, the search engine recommends similar datasets. We solve this dataset recommendation task using a similarity approach. We provide a simple benchmark task to evaluate different approaches for this dataset recommendation task. We also evaluate the recommendation task with several similarity approaches in the biomedical domain. We benchmark 8 different similarity metrics between datasets, including both ontology-based techniques and techniques from machine learning. Our results show that the task of recommending scientific datasets based on meta-data as it occurs in realistic dataset collections is a hard task. None of the ontology-based methods manage to perform well on this task, and are outscored by the majority of the machine-learning methods. Of these ML methods only one of the approaches performs reasonably well, and even then only reaches 70% accuracy.

@article{DBLP:journals/hisas/LiuPWFHH21,
  author    = {Ting Liu and
               Xueli Pan and
               Xu Wang and
               K. Anton Feenstra and
               Jaap Heringa and
               Zhisheng Huang},
  title     = {Predicting the relationships between gut microbiota and mental disorders
               with knowledge graphs},
  journal   = {Health Inf. Sci. Syst.},
  volume    = {9},
  number    = {1},
  pages     = {3},
  year      = {2021},
  url       = {https://doi.org/10.1007/s13755-020-00128-2},
  doi       = {10.1007/s13755-020-00128-2},
  timestamp = {Fri, 14 May 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/hisas/LiuPWFHH21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/hisas/HuangHLMWL21,
  author    = {Zhisheng Huang and
               Qing Hu and
               Mingqun Liao and
               Cong Miao and
               Chengyi Wang and
               Guanghua Liu},
  title     = {Knowledge Graphs of Kawasaki Disease},
  journal   = {Health Inf. Sci. Syst.},
  volume    = {9},
  number    = {1},
  pages     = {11},
  year      = {2021},
  url       = {https://doi.org/10.1007/s13755-020-00130-8},
  doi       = {10.1007/s13755-020-00130-8},
  timestamp = {Fri, 14 May 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/hisas/HuangHLMWL21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/hisas/SiulyAKHZ21,
  author    = {Siuly Siuly and
               Uwe Aickelin and
               Md. Enamul Kabir and
               Zhisheng Huang and
               Yanchun Zhang},
  title     = {Guest Editorial: Special issue on "Artificial Intelligence in
               Health Informatics"},
  journal   = {Health Inf. Sci. Syst.},
  volume    = {9},
  number    = {1},
  pages     = {23},
  year      = {2021},
  url       = {https://doi.org/10.1007/s13755-021-00155-7},
  doi       = {10.1007/s13755-021-00155-7},
  timestamp = {Fri, 18 Jun 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/hisas/SiulyAKHZ21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/data/WangHH21,
  author    = {Xu Wang and
               Frank van Harmelen and
               Zhisheng Huang},
  editor    = {Christoph Quix and
               Slimane Hammoudi and
               Wil M. P. van der Aalst},
  title     = {Biomedical Dataset Recommendation},
  booktitle = {Proceedings of the 10th International Conference on Data Science,
               Technology and Applications, {DATA} 2021, Online Streaming, July 6-8,
               2021},
  pages     = {192--199},
  publisher = {{SCITEPRESS}},
  year      = {2021},
  url       = {https://doi.org/10.5220/0010521801920199},
  doi       = {10.5220/0010521801920199},
  timestamp = {Wed, 28 Jul 2021 15:47:03 +0200},
  biburl    = {https://dblp.org/rec/conf/data/WangHH21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/fgcs/HortaTLM21,
  author    = {Vitor A. C. Horta and
               Ilaria Tiddi and
               Suzanne Little and
               Alessandra Mileo},
  title     = {Extracting knowledge from Deep Neural Networks through graph analysis},
  journal   = {Future Gener. Comput. Syst.},
  volume    = {120},
  pages     = {109--118},
  year      = {2021},
  url       = {https://doi.org/10.1016/j.future.2021.02.009},
  doi       = {10.1016/j.future.2021.02.009},
  timestamp = {Fri, 14 May 2021 08:31:00 +0200},
  biburl    = {https://dblp.org/rec/journals/fgcs/HortaTLM21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/aaaiss/BoomgaardSTSS21,
  author    = {Guusje Boomgaard and
               Selene Baez Santamar{\'{\i}}a and
               Ilaria Tiddi and
               Robert{-}Jan Sips and
               Zolt{\'{a}}n Szl{\'{a}}vik},
  editor    = {Andreas Martin and
               Knut Hinkelmann and
               Hans{-}Georg Fill and
               Aurona Gerber and
               Doug Lenat and
               Reinhard Stolle and
               Frank van Harmelen},
  title     = {Learning Profile-Based Recommendations for Medical Search Auto-Complete},
  booktitle = {Proceedings of the {AAAI} 2021 Spring Symposium on Combining Machine
               Learning and Knowledge Engineering {(AAAI-MAKE} 2021), Stanford University,
               Palo Alto, California, USA, March 22-24, 2021},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2846},
  publisher = {CEUR-WS.org},
  year      = {2021},
  url       = {http://ceur-ws.org/Vol-2846/paper34.pdf},
  timestamp = {Thu, 22 Apr 2021 15:54:03 +0200},
  biburl    = {https://dblp.org/rec/conf/aaaiss/BoomgaardSTSS21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/esws/HaanTB21,
  author    = {Rosaline de Haan and
               Ilaria Tiddi and
               Wouter Beek},
  editor    = {Ruben Verborgh and
               Katja Hose and
               Heiko Paulheim and
               Pierre{-}Antoine Champin and
               Maria Maleshkova and
               {\'{O}}scar Corcho and
               Petar Ristoski and
               Mehwish Alam},
  title     = {Discovering Research Hypotheses in Social Science Using Knowledge
               Graph Embeddings},
  booktitle = {The Semantic Web - 18th International Conference, {ESWC} 2021, Virtual
               Event, June 6-10, 2021, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {12731},
  pages     = {477--494},
  publisher = {Springer},
  year      = {2021},
  url       = {https://doi.org/10.1007/978-3-030-77385-4\_28},
  doi       = {10.1007/978-3-030-77385-4\_28},
  timestamp = {Tue, 22 Jun 2021 14:39:38 +0200},
  biburl    = {https://dblp.org/rec/conf/esws/HaanTB21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/access/KarimJDCBRD21,
  author    = {Md. Rezaul Karim and
               Jiao Jiao and
               Till D{\"{o}}hmen and
               Michael Cochez and
               Oya Beyan and
               Dietrich Rebholz{-}Schuhmann and
               Stefan Decker},
  title     = {DeepKneeExplainer: Explainable Knee Osteoarthritis Diagnosis From
               Radiographs and Magnetic Resonance Imaging},
  journal   = {{IEEE} Access},
  volume    = {9},
  pages     = {39757--39780},
  year      = {2021},
  url       = {https://doi.org/10.1109/ACCESS.2021.3062493},
  doi       = {10.1109/ACCESS.2021.3062493},
  timestamp = {Wed, 07 Apr 2021 15:59:12 +0200},
  biburl    = {https://dblp.org/rec/journals/access/KarimJDCBRD21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/bib/KarimBZCRCD21,
  author    = {Md. Rezaul Karim and
               Oya Beyan and
               Achille Zappa and
               Ivan G. Costa and
               Dietrich Rebholz{-}Schuhmann and
               Michael Cochez and
               Stefan Decker},
  title     = {Deep learning-based clustering approaches for bioinformatics},
  journal   = {Briefings Bioinform.},
  volume    = {22},
  number    = {1},
  pages     = {393--415},
  year      = {2021},
  url       = {https://doi.org/10.1093/bib/bbz170},
  doi       = {10.1093/bib/bbz170},
  timestamp = {Tue, 01 Jun 2021 15:19:50 +0200},
  biburl    = {https://dblp.org/rec/journals/bib/KarimBZCRCD21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/gkr/2020,
  editor    = {Michael Cochez and
               Madalina Croitoru and
               Pierre Marquis and
               Sebastian Rudolph},
  title     = {Graph Structures for Knowledge Representation and Reasoning - 6th
               International Workshop, {GKR} 2020, Virtual Event, September 5, 2020,
               Revised Selected Papers},
  series    = {Lecture Notes in Computer Science},
  volume    = {12640},
  publisher = {Springer},
  year      = {2021},
  url       = {https://doi.org/10.1007/978-3-030-72308-8},
  doi       = {10.1007/978-3-030-72308-8},
  isbn      = {978-3-030-72307-1},
  timestamp = {Fri, 14 May 2021 08:34:16 +0200},
  biburl    = {https://dblp.org/rec/conf/gkr/2020.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2102-11389,
  author    = {Ruud van Bakel and
               Teodor Aleksiev and
               Daniel Daza and
               Dimitrios Alivanistos and
               Michael Cochez},
  title     = {Approximate Knowledge Graph Query Answering: From Ranking to Binary
               Classification},
  journal   = {CoRR},
  volume    = {abs/2102.11389},
  year      = {2021},
  url       = {https://arxiv.org/abs/2102.11389},
  archivePrefix = {arXiv},
  eprint    = {2102.11389},
  timestamp = {Wed, 24 Feb 2021 15:42:45 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2102-11389.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2103-00082,
  author    = {Leandro Eichenberger and
               Michael Cochez and
               Benjamin Heitmann and
               Stefan Decker},
  title     = {Secure Evaluation of Knowledge Graph Merging Gain},
  journal   = {CoRR},
  volume    = {abs/2103.00082},
  year      = {2021},
  url       = {https://arxiv.org/abs/2103.00082},
  archivePrefix = {arXiv},
  eprint    = {2103.00082},
  timestamp = {Thu, 04 Mar 2021 17:00:40 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2103-00082.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2106-08166,
  author    = {Dimitrios Alivanistos and
               Max Berrendorf and
               Michael Cochez and
               Mikhail Galkin},
  title     = {Query Embedding on Hyper-relational Knowledge Graphs},
  journal   = {CoRR},
  volume    = {abs/2106.08166},
  year      = {2021},
  url       = {https://arxiv.org/abs/2106.08166},
  archivePrefix = {arXiv},
  eprint    = {2106.08166},
  timestamp = {Tue, 29 Jun 2021 16:55:04 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2106-08166.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{peter2020kgbench,
	title={kgbench: A Collection of Datasets for Multimodal and Relational Learning on Heterogeneous Knowledge},
	author={Wilcke, WX and Bloem, P and van Berkel, L and de Boer, V},
	booktitle={Proceedings of the European Semantic Web Conference 2021},
	pages={614--630},
    year={2021},
    organization={Springer}
}

@inproceedings{daza2021inductive,
    author = {Daniel Daza and Michael Cochez and Paul Groth},
    title = {Inductive Entity Representations from Text via Link Prediction},
    year = {2021},
    isbn = {9781450383127},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3442381.3450141},
    doi = {10.1145/3442381.3450141},
    booktitle = {Proceedings of The Web Conference 2021},
    location = {Ljubljana, Slovenia},
    series = {WWW '21}
}

@inproceedings{arakelyan2021complex,
  author    = {Erik Arakelyan and
               Daniel Daza and
               Pasquale Minervini and
               Michael Cochez},
  title     = {Complex Query Answering with Neural Link Predictors},
  booktitle = {9th International Conference on Learning Representations, {ICLR} 2021,
               Virtual Event, Austria, May 3-7, 2021},
  publisher = {OpenReview.net},
  year      = {2021},
  url       = {https://openreview.net/forum?id=Mos9F9kDwkz}
}

@INPROCEEDINGS{karim2020deepcovidexplainer,
  author={Karim, Md. Rezaul and Döhmen, Till and Cochez, Michael and Beyan, Oya and Rebholz-Schuhmann, Dietrich and Decker, Stefan},
  booktitle={2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
  title={DeepCOVIDExplainer: Explainable COVID-19 Diagnosis from Chest X-ray Images},
  year={2020},
  volume={},
  number={},
  pages={1034-1037},
  url={https://arxiv.org/abs/2004.04582},
  doi={10.1109/BIBM49941.2020.9313304}}

  

@InProceedings{gleim2020schematree,
	author="Gleim, Lars C.
	and Schimassek, Rafael
	and H{\"u}ser, Dominik
	and Peters, Maximilian
	and Kr{\"a}mer, Christoph
	and Cochez, Michael
	and Decker, Stefan",
	editor="Harth, Andreas
	and Kirrane, Sabrina
	and Ngonga Ngomo, Axel-Cyrille
	and Paulheim, Heiko
	and Rula, Anisa
	and Gentile, Anna Lisa
	and Haase, Peter
	and Cochez, Michael",
	title="SchemaTree: Maximum-Likelihood Property Recommendation for {Wikidata}",
	booktitle="The Semantic Web",
	year="2020",
	publisher="Springer International Publishing",
	address="Cham",
	pages="179--195",
	abstract="Wikidata is a free and open knowledge base which can be read and edited by both humans and machines. It acts as a central storage for the structured data of several Wikimedia projects. To improve the process of manually inserting new facts, the Wikidata platform features an association rule-based tool to recommend additional suitable properties. In this work, we introduce a novel approach to provide such recommendations based on frequentist inference. We introduce a trie-based method that can efficiently learn and represent property set probabilities in RDF graphs. We extend the method by adding type information to improve recommendation precision and introduce backoff strategies which further increase the performance of the initial approach for entities with rare property combinations. We investigate how the captured structure can be employed for property recommendation, analogously to the Wikidata PropertySuggester. We evaluate our approach on the full Wikidata dataset and compare its performance to the state-of-the-art Wikidata PropertySuggester, outperforming it in all evaluated metrics. Notably we could reduce the average rank of the first relevant recommendation by 71{\%}.",
	isbn="978-3-030-49461-2"
}

Wikidata is a free and open knowledge base which can be read and edited by both humans and machines. It acts as a central storage for the structured data of several Wikimedia projects. To improve the process of manually inserting new facts, the Wikidata platform features an association rule-based tool to recommend additional suitable properties. In this work, we introduce a novel approach to provide such recommendations based on frequentist inference. We introduce a trie-based method that can efficiently learn and represent property set probabilities in RDF graphs. We extend the method by adding type information to improve recommendation precision and introduce backoff strategies which further increase the performance of the initial approach for entities with rare property combinations. We investigate how the captured structure can be employed for property recommendation, analogously to the Wikidata PropertySuggester. We evaluate our approach on the full Wikidata dataset and compare its performance to the state-of-the-art Wikidata PropertySuggester, outperforming it in all evaluated metrics. Notably we could reduce the average rank of the first relevant recommendation by 71%.

@Article{wang2020queryconstruct,
	author={Wang, Ruijie
	and Wang, Meng
	and Liu, Jun
	and Cochez, Michael
	and Decker, Stefan},
	title={Structured query construction via knowledge graph embedding},
	journal={Knowledge and Information Systems},
	year={2020},
	month={May},
	day={01},
	volume={62},
	number={5},
	pages={1819-1846},
	abstract={In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs. Given a natural language question, the learned embedding representations of the knowledge graph are utilized to compute the query structure and assemble vertices/edges into the target query. Extensive experiments were conducted on the benchmark dataset, and the results demonstrate that our framework outperforms state-of-the-art baseline models regarding effectiveness and efficiency.},
	issn={0219-3116},
	doi={10.1007/s10115-019-01401-x},
	url={https://doi.org/10.1007/s10115-019-01401-x}
}

In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs. Given a natural language question, the learned embedding representations of the knowledge graph are utilized to compute the query structure and assemble vertices/edges into the target query. Extensive experiments were conducted on the benchmark dataset, and the results demonstrate that our framework outperforms state-of-the-art baseline models regarding effectiveness and efficiency.

@article{hallawa2020morphological,
	author = { Ahmed   Hallawa  and  Giovanni   Iacca  and  Cagatay   Sariman  and  Touhidur   Rahman  and  Michael   Cochez  and  Gerd   Ascheid },
	title = {Morphological evolution for pipe inspection using Robot Operating System ({ROS})},
	journal = {Materials and Manufacturing Processes},
	volume = {35},
	number = {6},
	pages = {714-724},
	year  = {2020},
	publisher = {Taylor & Francis},
	doi = {10.1080/10426914.2020.1746335},

	URL = {
	https://doi.org/10.1080/10426914.2020.1746335

	},
	eprint = {
	https://doi.org/10.1080/10426914.2020.1746335

	}

}

@INPROCEEDINGS{karim2020classification,
  author={Karim, Md. Rezaul and Raja Chakravarthi, Bharathi and McCrae, John P. and Cochez, Michael},
  booktitle={2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)},
  title={Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network},
  year={2020},
  pages={390-399},
  doi={10.1109/DSAA49011.2020.00053},
  url={https://arxiv.org/abs/2004.07807},

  }

@article{DBLP:journals/computer/AkataBRDDEFGHHH20,
  author    = {Zeynep Akata and
               Dan Balliet and
               Maarten de Rijke and
               Frank Dignum and
               Virginia Dignum and
               Guszti Eiben and
               Antske Fokkens and
               Davide Grossi and
               Koen V. Hindriks and
               Holger H. Hoos and
               Hayley Hung and
               Catholijn M. Jonker and
               Christof Monz and
               Mark A. Neerincx and
               Frans A. Oliehoek and
               Henry Prakken and
               Stefan Schlobach and
               Linda C. van der Gaag and
               Frank van Harmelen and
               Herke van Hoof and
               Birna van Riemsdijk and
               Aimee van Wynsberghe and
               Rineke Verbrugge and
               Bart Verheij and
               Piek Vossen and
               Max Welling},
  title     = {A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect
               With Collaborative, Adaptive, Responsible, and Explainable Artificial
               Intelligence},
  journal   = {Computer},
  volume    = {53},
  number    = {8},
  pages     = {18--28},
  year      = {2020},
  url       = {https://doi.org/10.1109/MC.2020.2996587},
  doi       = {10.1109/MC.2020.2996587},
  timestamp = {Fri, 09 Apr 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/computer/AkataBRDDEFGHHH20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/dint/RaadBHWPS20,
  author    = {Joe Raad and
               Wouter Beek and
               Frank van Harmelen and
               Jan Wielemaker and
               Nathalie Pernelle and
               Fatiha Sa{\"{\i}}s},
  title     = {Constructing and Cleaning Identity Graphs in the {LOD} Cloud},
  journal   = {Data Intell.},
  volume    = {2},
  number    = {3},
  pages     = {323--352},
  year      = {2020},
  url       = {https://doi.org/10.1162/dint\_a\_00057},
  doi       = {10.1162/dint\_a\_00057},
  timestamp = {Sat, 05 Sep 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/dint/RaadBHWPS20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/esws/BazoobandiBHU20,
  author    = {Hamid R. Bazoobandi and
               Henri E. Bal and
               Frank van Harmelen and
               Jacopo Urbani},
  editor    = {Andreas Harth and
               Sabrina Kirrane and
               Axel{-}Cyrille Ngonga Ngomo and
               Heiko Paulheim and
               Anisa Rula and
               Anna Lisa Gentile and
               Peter Haase and
               Michael Cochez},
  title     = {Handling Impossible Derivations During Stream Reasoning},
  booktitle = {The Semantic Web - 17th International Conference, {ESWC} 2020, Heraklion,
               Crete, Greece, May 31-June 4, 2020, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {12123},
  pages     = {3--19},
  publisher = {Springer},
  year      = {2020},
  url       = {https://doi.org/10.1007/978-3-030-49461-2\_1},
  doi       = {10.1007/978-3-030-49461-2\_1},
  timestamp = {Sat, 05 Sep 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/esws/BazoobandiBHU20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/esws/BeekRAH20,
  author    = {Wouter Beek and
               Joe Raad and
               Erman Acar and
               Frank van Harmelen},
  editor    = {Andreas Harth and
               Sabrina Kirrane and
               Axel{-}Cyrille Ngonga Ngomo and
               Heiko Paulheim and
               Anisa Rula and
               Anna Lisa Gentile and
               Peter Haase and
               Michael Cochez},
  title     = {MetaLink: {A} Travel Guide to the {LOD} Cloud},
  booktitle = {The Semantic Web - 17th International Conference, {ESWC} 2020, Heraklion,
               Crete, Greece, May 31-June 4, 2020, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {12123},
  pages     = {481--496},
  publisher = {Springer},
  year      = {2020},
  url       = {https://doi.org/10.1007/978-3-030-49461-2\_28},
  doi       = {10.1007/978-3-030-49461-2\_28},
  timestamp = {Sat, 05 Sep 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/esws/BeekRAH20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/his2/WangHH20,
  author    = {Xu Wang and
               Zhisheng Huang and
               Frank van Harmelen},
  editor    = {Zhisheng Huang and
               Siuly Siuly and
               Hua Wang and
               Rui Zhou and
               Yanchun Zhang},
  title     = {Ontology-Based Semantic Similarity Approach for Biomedical Dataset
               Retrieval},
  booktitle = {Health Information Science - 9th International Conference, {HIS} 2020,
               Amsterdam, The Netherlands, October 20-23, 2020, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {12435},
  pages     = {49--60},
  publisher = {Springer},
  year      = {2020},
  url       = {https://doi.org/10.1007/978-3-030-61951-0\_5},
  doi       = {10.1007/978-3-030-61951-0\_5},
  timestamp = {Tue, 29 Dec 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/his2/WangHH20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/ic3k/Harmelen20,
  author    = {Frank van Harmelen},
  editor    = {Ana L. N. Fred and
               Joaquim Filipe},
  title     = {Hybrid Intelligence: {AI} Systems That Collaborate with People Instead
               of Replacing Them},
  booktitle = {Proceedings of the 12th International Joint Conference on Knowledge
               Discovery, Knowledge Engineering and Knowledge Management, {IC3K}
               2020, Volume 1: KDIR, Budapest, Hungary, November 2-4, 2020},
  pages     = {21},
  publisher = {{SCITEPRESS}},
  year      = {2020},
  timestamp = {Mon, 23 Nov 2020 16:26:23 +0100},
  biburl    = {https://dblp.org/rec/conf/ic3k/Harmelen20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/ic3k/WilckeKBSH20,
  author    = {Xander Wilcke and
               Maurice de Kleijn and
               Victor de Boer and
               Henk J. Scholten and
               Frank van Harmelen},
  editor    = {Ana L. N. Fred and
               Joaquim Filipe},
  title     = {Bottom-up Discovery of Context-aware Quality Constraints for Heterogeneous
               Knowledge Graphs},
  booktitle = {Proceedings of the 12th International Joint Conference on Knowledge
               Discovery, Knowledge Engineering and Knowledge Management, {IC3K}
               2020, Volume 1: KDIR, Budapest, Hungary, November 2-4, 2020},
  pages     = {81--92},
  publisher = {{SCITEPRESS}},
  year      = {2020},
  url       = {https://doi.org/10.5220/0010113500810092},
  doi       = {10.5220/0010113500810092},
  timestamp = {Tue, 29 Dec 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/ic3k/WilckeKBSH20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/ic3k/WangHH20,
  author    = {Xu Wang and
               Frank van Harmelen and
               Zhisheng Huang},
  editor    = {Ana L. N. Fred and
               Joaquim Filipe},
  title     = {Ontology-based Methods for Classifying Scientific Datasets into Research
               Domains: Much Harder than Expected},
  booktitle = {Proceedings of the 12th International Joint Conference on Knowledge
               Discovery, Knowledge Engineering and Knowledge Management, {IC3K}
               2020, Volume 1: KDIR, Budapest, Hungary, November 2-4, 2020},
  pages     = {153--160},
  publisher = {{SCITEPRESS}},
  year      = {2020},
  url       = {https://doi.org/10.5220/0010056101530160},
  doi       = {10.5220/0010056101530160},
  timestamp = {Tue, 29 Dec 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/ic3k/WangHH20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/kr/KriekenAH20,
  author    = {Emile van Krieken and
               Erman Acar and
               Frank van Harmelen},
  editor    = {Diego Calvanese and
               Esra Erdem and
               Michael Thielscher},
  title     = {Analyzing Differentiable Fuzzy Implications},
  booktitle = {Proceedings of the 17th International Conference on Principles of
               Knowledge Representation and Reasoning, {KR} 2020, Rhodes, Greece,
               September 12-18, 2020},
  pages     = {893--903},
  year      = {2020},
  url       = {https://doi.org/10.24963/kr.2020/92},
  doi       = {10.24963/kr.2020/92},
  timestamp = {Fri, 09 Apr 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/kr/KriekenAH20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/webist/Harmelen20,
  author    = {Frank van Harmelen},
  editor    = {Massimo Marchiori and
               Francisco Jos{\'{e}} Dom{\'{\i}}nguez Mayo and
               Joaquim Filipe},
  title     = {Hybrid Intelligence: {AI} Systems that Collaborate with People Instead
               of Replacing Them},
  booktitle = {Proceedings of the 16th International Conference on Web Information
               Systems and Technologies, {WEBIST} 2020, Budapest, Hungary, November
               3-5, 2020},
  pages     = {9},
  publisher = {{SCITEPRESS}},
  year      = {2020},
  timestamp = {Mon, 23 Nov 2020 16:44:06 +0100},
  biburl    = {https://dblp.org/rec/conf/webist/Harmelen20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/wise/WangHH20,
  author    = {Xu Wang and
               Zhisheng Huang and
               Frank van Harmelen},
  editor    = {Zhisheng Huang and
               Wouter Beek and
               Hua Wang and
               Rui Zhou and
               Yanchun Zhang},
  title     = {Evaluating Similarity Measures for Dataset Search},
  booktitle = {Web Information Systems Engineering - {WISE} 2020 - 21st International
               Conference, Amsterdam, The Netherlands, October 20-24, 2020, Proceedings,
               Part {II}},
  series    = {Lecture Notes in Computer Science},
  volume    = {12343},
  pages     = {38--51},
  publisher = {Springer},
  year      = {2020},
  url       = {https://doi.org/10.1007/978-3-030-62008-0\_3},
  doi       = {10.1007/978-3-030-62008-0\_3},
  timestamp = {Tue, 29 Dec 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/wise/WangHH20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/aaaiss/2020make1,
  editor    = {Andreas Martin and
               Knut Hinkelmann and
               Hans{-}Georg Fill and
               Aurona Gerber and
               Doug Lenat and
               Reinhard Stolle and
               Frank van Harmelen},
  title     = {Proceedings of the {AAAI} 2020 Spring Symposium on Combining Machine
               Learning and Knowledge Engineering in Practice, {AAAI-MAKE} 2020,
               Palo Alto, CA, USA, March 23-25, 2020, Volume {I}},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2600},
  publisher = {CEUR-WS.org},
  year      = {2020},
  url       = {http://ceur-ws.org/Vol-2600},
  urn       = {urn:nbn:de:0074-2600-2},
  timestamp = {Mon, 08 Feb 2021 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/aaaiss/2020make1.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2002-06100,
  author    = {Emile van Krieken and
               Erman Acar and
               Frank van Harmelen},
  title     = {Analyzing Differentiable Fuzzy Logic Operators},
  journal   = {CoRR},
  volume    = {abs/2002.06100},
  year      = {2020},
  url       = {https://arxiv.org/abs/2002.06100},
  archivePrefix = {arXiv},
  eprint    = {2002.06100},
  timestamp = {Sat, 23 Jan 2021 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2002-06100.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2003-12383,
  author    = {W. X. Wilcke and
               Peter Bloem and
               Victor de Boer and
               R. H. van t Veer and
               F. A. H. van Harmelen},
  title     = {End-to-End Entity Classification on Multimodal Knowledge Graphs},
  journal   = {CoRR},
  volume    = {abs/2003.12383},
  year      = {2020},
  url       = {https://arxiv.org/abs/2003.12383},
  archivePrefix = {arXiv},
  eprint    = {2003.12383},
  timestamp = {Mon, 23 Nov 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2003-12383.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2006-03472,
  author    = {Emile van Krieken and
               Erman Acar and
               Frank van Harmelen},
  title     = {Analyzing Differentiable Fuzzy Implications},
  journal   = {CoRR},
  volume    = {abs/2006.03472},
  year      = {2020},
  url       = {https://arxiv.org/abs/2006.03472},
  archivePrefix = {arXiv},
  eprint    = {2006.03472},
  timestamp = {Sat, 23 Jan 2021 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2006-03472.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/esws/TiddiBT20,
  author    = {Ilaria Tiddi and
               Daniel Balliet and
               Annette ten Teije},
  editor    = {Andreas Harth and
               Sabrina Kirrane and
               Axel{-}Cyrille Ngonga Ngomo and
               Heiko Paulheim and
               Anisa Rula and
               Anna Lisa Gentile and
               Peter Haase and
               Michael Cochez},
  title     = {Fostering Scientific Meta-analyses with Knowledge Graphs: {A} Case-Study},
  booktitle = {The Semantic Web - 17th International Conference, {ESWC} 2020, Heraklion,
               Crete, Greece, May 31-June 4, 2020, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {12123},
  pages     = {287--303},
  publisher = {Springer},
  year      = {2020},
  url       = {https://doi.org/10.1007/978-3-030-49461-2\_17},
  doi       = {10.1007/978-3-030-49461-2\_17},
  timestamp = {Sat, 05 Sep 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/esws/TiddiBT20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/mie/JansenGMHTM20,
  author    = {Tom Jansen and
               Gijs Geleijnse and
               Marissa Van Maaren and
               Mathijs P. Hendriks and
               Annette ten Teije and
               Arturo Moncada{-}Torres},
  editor    = {Louise Bilenberg Pape{-}Haugaard and
               Christian Lovis and
               Inge Cort Madsen and
               Patrick Weber and
               Per Hostrup Nielsen and
               Philip Scott},
  title     = {Machine Learning Explainability in Breast Cancer Survival},
  booktitle = {Digital Personalized Health and Medicine - Proceedings of {MIE} 2020,
               Medical Informatics Europe, Geneva, Switzerland, April 28 - May 1,
               2020},
  series    = {Studies in Health Technology and Informatics},
  volume    = {270},
  pages     = {307--311},
  publisher = {{IOS} Press},
  year      = {2020},
  url       = {https://doi.org/10.3233/SHTI200172},
  doi       = {10.3233/SHTI200172},
  timestamp = {Wed, 03 Feb 2021 08:36:32 +0100},
  biburl    = {https://dblp.org/rec/conf/mie/JansenGMHTM20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/tjs/LiZYHLCPW20,
  author    = {Jianqiang Li and
               Shenhe Zhao and
               Ji{-}Jiang Yang and
               Zhisheng Huang and
               Bo Liu and
               Shi Chen and
               Hui Pan and
               Qing Wang},
  title     = {{WCP-RNN:} a novel RNN-based approach for Bio-NER in Chinese EMRs},
  journal   = {J. Supercomput.},
  volume    = {76},
  number    = {3},
  pages     = {1450--1467},
  year      = {2020},
  url       = {https://doi.org/10.1007/s11227-017-2229-x},
  doi       = {10.1007/s11227-017-2229-x},
  timestamp = {Tue, 17 Nov 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/tjs/LiZYHLCPW20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/his2/JingLH20,
  author    = {Xiaomin Jing and
               Shaofu Lin and
               Zhisheng Huang},
  editor    = {Zhisheng Huang and
               Siuly Siuly and
               Hua Wang and
               Rui Zhou and
               Yanchun Zhang},
  title     = {Research on the Behavior Pattern of Microblog "Tree Hole"
               Users with Their Temporal Characteristics},
  booktitle = {Health Information Science - 9th International Conference, {HIS} 2020,
               Amsterdam, The Netherlands, October 20-23, 2020, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {12435},
  pages     = {25--34},
  publisher = {Springer},
  year      = {2020},
  url       = {https://doi.org/10.1007/978-3-030-61951-0\_3},
  doi       = {10.1007/978-3-030-61951-0\_3},
  timestamp = {Wed, 21 Oct 2020 16:49:35 +0200},
  biburl    = {https://dblp.org/rec/conf/his2/JingLH20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/his2/WangHH20,
  author    = {Xu Wang and
               Zhisheng Huang and
               Frank van Harmelen},
  editor    = {Zhisheng Huang and
               Siuly Siuly and
               Hua Wang and
               Rui Zhou and
               Yanchun Zhang},
  title     = {Ontology-Based Semantic Similarity Approach for Biomedical Dataset
               Retrieval},
  booktitle = {Health Information Science - 9th International Conference, {HIS} 2020,
               Amsterdam, The Netherlands, October 20-23, 2020, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {12435},
  pages     = {49--60},
  publisher = {Springer},
  year      = {2020},
  url       = {https://doi.org/10.1007/978-3-030-61951-0\_5},
  doi       = {10.1007/978-3-030-61951-0\_5},
  timestamp = {Tue, 29 Dec 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/his2/WangHH20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/ic3k/WangHH20,
  author    = {Xu Wang and
               Frank van Harmelen and
               Zhisheng Huang},
  editor    = {Ana L. N. Fred and
               Joaquim Filipe},
  title     = {Ontology-based Methods for Classifying Scientific Datasets into Research
               Domains: Much Harder than Expected},
  booktitle = {Proceedings of the 12th International Joint Conference on Knowledge
               Discovery, Knowledge Engineering and Knowledge Management, {IC3K}
               2020, Volume 1: KDIR, Budapest, Hungary, November 2-4, 2020},
  pages     = {153--160},
  publisher = {{SCITEPRESS}},
  year      = {2020},
  url       = {https://doi.org/10.5220/0010056101530160},
  doi       = {10.5220/0010056101530160},
  timestamp = {Tue, 29 Dec 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/ic3k/WangHH20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/wise/WangHH20,
  author    = {Xu Wang and
               Zhisheng Huang and
               Frank van Harmelen},
  editor    = {Zhisheng Huang and
               Wouter Beek and
               Hua Wang and
               Rui Zhou and
               Yanchun Zhang},
  title     = {Evaluating Similarity Measures for Dataset Search},
  booktitle = {Web Information Systems Engineering - {WISE} 2020 - 21st International
               Conference, Amsterdam, The Netherlands, October 20-24, 2020, Proceedings,
               Part {II}},
  series    = {Lecture Notes in Computer Science},
  volume    = {12343},
  pages     = {38--51},
  publisher = {Springer},
  year      = {2020},
  url       = {https://doi.org/10.1007/978-3-030-62008-0\_3},
  doi       = {10.1007/978-3-030-62008-0\_3},
  timestamp = {Tue, 29 Dec 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/wise/WangHH20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/his2/2020,
  editor    = {Zhisheng Huang and
               Siuly Siuly and
               Hua Wang and
               Rui Zhou and
               Yanchun Zhang},
  title     = {Health Information Science - 9th International Conference, {HIS} 2020,
               Amsterdam, The Netherlands, October 20-23, 2020, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {12435},
  publisher = {Springer},
  year      = {2020},
  url       = {https://doi.org/10.1007/978-3-030-61951-0},
  doi       = {10.1007/978-3-030-61951-0},
  isbn      = {978-3-030-61950-3},
  timestamp = {Wed, 21 Oct 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/his2/2020.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/wise/2020-1,
  editor    = {Zhisheng Huang and
               Wouter Beek and
               Hua Wang and
               Rui Zhou and
               Yanchun Zhang},
  title     = {Web Information Systems Engineering - {WISE} 2020 - 21st International
               Conference, Amsterdam, The Netherlands, October 20-24, 2020, Proceedings,
               Part {I}},
  series    = {Lecture Notes in Computer Science},
  volume    = {12342},
  publisher = {Springer},
  year      = {2020},
  url       = {https://doi.org/10.1007/978-3-030-62005-9},
  doi       = {10.1007/978-3-030-62005-9},
  isbn      = {978-3-030-62004-2},
  timestamp = {Fri, 25 Dec 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/wise/2020-1.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/wise/2020-2,
  editor    = {Zhisheng Huang and
               Wouter Beek and
               Hua Wang and
               Rui Zhou and
               Yanchun Zhang},
  title     = {Web Information Systems Engineering - {WISE} 2020 - 21st International
               Conference, Amsterdam, The Netherlands, October 20-24, 2020, Proceedings,
               Part {II}},
  series    = {Lecture Notes in Computer Science},
  volume    = {12343},
  publisher = {Springer},
  year      = {2020},
  url       = {https://doi.org/10.1007/978-3-030-62008-0},
  doi       = {10.1007/978-3-030-62008-0},
  isbn      = {978-3-030-62007-3},
  timestamp = {Thu, 22 Oct 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/wise/2020-2.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/ijsr/TiddiBDdM20,
  author    = {Ilaria Tiddi and
               Emanuele Bastianelli and
               Enrico Daga and
               Mathieu d'Aquin and
               Enrico Motta},
  title     = {Robot-City Interaction: Mapping the Research Landscape - {A} Survey
               of the Interactions Between Robots and Modern Cities},
  journal   = {Int. J. Soc. Robotics},
  volume    = {12},
  number    = {2},
  pages     = {299--324},
  year      = {2020},
  url       = {https://doi.org/10.1007/s12369-019-00534-x},
  doi       = {10.1007/s12369-019-00534-x},
  timestamp = {Fri, 14 May 2021 08:32:30 +0200},
  biburl    = {https://dblp.org/rec/journals/ijsr/TiddiBDdM20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/aaaiss/MensioBT020,
  author    = {Martino Mensio and
               Emanuele Bastianelli and
               Ilaria Tiddi and
               Giuseppe Rizzo},
  editor    = {Andreas Martin and
               Knut Hinkelmann and
               Hans{-}Georg Fill and
               Aurona Gerber and
               Doug Lenat and
               Reinhard Stolle and
               Frank van Harmelen},
  title     = {Mitigating Bias in Deep Nets with Knowledge Bases: the Case of Natural
               Language Understanding for Robots},
  booktitle = {Proceedings of the {AAAI} 2020 Spring Symposium on Combining Machine
               Learning and Knowledge Engineering in Practice, {AAAI-MAKE} 2020,
               Palo Alto, CA, USA, March 23-25, 2020, Volume {I}},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2600},
  publisher = {CEUR-WS.org},
  year      = {2020},
  url       = {http://ceur-ws.org/Vol-2600/paper20.pdf},
  timestamp = {Mon, 08 Feb 2021 14:04:28 +0100},
  biburl    = {https://dblp.org/rec/conf/aaaiss/MensioBT020.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/cikm/YangT20,
  author    = {Xinran Yang and
               Ilaria Tiddi},
  editor    = {Stefan Conrad and
               Ilaria Tiddi},
  title     = {Creative Storytelling with Language Models and Knowledge Graphs},
  booktitle = {Proceedings of the {CIKM} 2020 Workshops co-located with 29th {ACM}
               International Conference on Information and Knowledge Management {(CIKM}
               2020), Galway, Ireland, October 19-23, 2020},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2699},
  publisher = {CEUR-WS.org},
  year      = {2020},
  url       = {http://ceur-ws.org/Vol-2699/paper07.pdf},
  timestamp = {Wed, 21 Oct 2020 19:01:41 +0200},
  biburl    = {https://dblp.org/rec/conf/cikm/YangT20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/esws/TiddiBT20,
  author    = {Ilaria Tiddi and
               Daniel Balliet and
               Annette ten Teije},
  editor    = {Andreas Harth and
               Sabrina Kirrane and
               Axel{-}Cyrille Ngonga Ngomo and
               Heiko Paulheim and
               Anisa Rula and
               Anna Lisa Gentile and
               Peter Haase and
               Michael Cochez},
  title     = {Fostering Scientific Meta-analyses with Knowledge Graphs: {A} Case-Study},
  booktitle = {The Semantic Web - 17th International Conference, {ESWC} 2020, Heraklion,
               Crete, Greece, May 31-June 4, 2020, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {12123},
  pages     = {287--303},
  publisher = {Springer},
  year      = {2020},
  url       = {https://doi.org/10.1007/978-3-030-49461-2\_17},
  doi       = {10.1007/978-3-030-49461-2\_17},
  timestamp = {Sat, 05 Sep 2020 18:05:40 +0200},
  biburl    = {https://dblp.org/rec/conf/esws/TiddiBT20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/jowo/KumpelGTB20,
  author    = {Michaela K{\"{u}}mpel and
               Anna de Groot and
               Ilaria Tiddi and
               Michael Beetz},
  editor    = {Karl Hammar and
               Oliver Kutz and
               Anastasia Dimou and
               Torsten Hahmann and
               Robert Hoehndorf and
               Claudio Masolo and
               Randi Vita and
               Sarra Ben Abb{\`{e}}s and
               Rim Hantach and
               Philippe Calvez and
               Tiago Prince Sales and
               Daniele Porello and
               Daniel Be{\ss}ler and
               Stefano Borgo and
               Mohammed Diab and
               Aldo Gangemi and
               Alberto Olivares Alarcos and
               Mihai Pomarlan and
               Robert Porzel and
               Martin G. Skj{\ae}veland and
               Daniel P. Lupp and
               Ian Horrocks and
               Johan W. Kl{\"{u}}wer and
               Christian Kindermann and
               Loris Bozzato and
               Till Mossakowski and
               Luciano Serafini},
  title     = {Using Linked Data to Help Robots Understand Product-related Actions},
  booktitle = {Proceedings of the Joint Ontology Workshops co-located with the Bolzano
               Summer of Knowledge {(BOSK} 2020), Virtual {\&} Bozen-Bolzano,
               Italy, August 31st to October 7th, 2020},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2708},
  publisher = {CEUR-WS.org},
  year      = {2020},
  url       = {http://ceur-ws.org/Vol-2708/robontics2.pdf},
  timestamp = {Thu, 29 Oct 2020 10:45:22 +0100},
  biburl    = {https://dblp.org/rec/conf/jowo/KumpelGTB20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@incollection{DBLP:series/ssw/TiddiR20,
  author    = {Ilaria Tiddi and
               Joe Raad},
  editor    = {Ilaria Tiddi and
               Freddy L{\'{e}}cu{\'{e}} and
               Pascal Hitzler},
  title     = {Managing Identity in Knowledge-Based Explainable Systems},
  booktitle = {Knowledge Graphs for eXplainable Artificial Intelligence: Foundations,
               Applications and Challenges},
  series    = {Studies on the Semantic Web},
  volume    = {47},
  pages     = {286--300},
  publisher = {{IOS} Press},
  year      = {2020},
  url       = {https://doi.org/10.3233/SSW200025},
  doi       = {10.3233/SSW200025},
  timestamp = {Thu, 13 Aug 2020 18:43:25 +0200},
  biburl    = {https://dblp.org/rec/series/ssw/TiddiR20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/cikm/2020w,
  editor    = {Stefan Conrad and
               Ilaria Tiddi},
  title     = {Proceedings of the {CIKM} 2020 Workshops co-located with 29th {ACM}
               International Conference on Information and Knowledge Management {(CIKM}
               2020), Galway, Ireland, October 19-23, 2020},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2699},
  publisher = {CEUR-WS.org},
  year      = {2020},
  url       = {http://ceur-ws.org/Vol-2699},
  urn       = {urn:nbn:de:0074-2699-4},
  timestamp = {Wed, 21 Oct 2020 19:01:41 +0200},
  biburl    = {https://dblp.org/rec/conf/cikm/2020w.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@book{DBLP:series/ssw/47,
  editor    = {Ilaria Tiddi and
               Freddy L{\'{e}}cu{\'{e}} and
               Pascal Hitzler},
  title     = {Knowledge Graphs for eXplainable Artificial Intelligence: Foundations,
               Applications and Challenges},
  series    = {Studies on the Semantic Web},
  volume    = {47},
  publisher = {{IOS} Press},
  year      = {2020},
  url       = {http://ebooks.iospress.nl/volume/knowledge-graphs-for-explainable-artificial-intelligence-foundations-applications-and-challenges},
  isbn      = {978-1-64368-080-4},
  timestamp = {Wed, 03 Jun 2020 15:28:32 +0200},
  biburl    = {https://dblp.org/rec/series/ssw/47.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{wilcke2020bottom,
	author={Wilcke, WX and de Kleijn, MTM and de Boer, V and Scholten, HJ and van Harmelen, FAH},
	title={Bottom-up Discovery of Context-aware Quality Constraints for Heterogeneous Knowledge Graphs},
	booktitle={Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
	year={2020},
	pages={81-92},
	publisher={SciTePress},
	organization={INSTICC},
	doi={10.5220/0010113500810092},
	isbn={978-989-758-474-9},
}

@article{DBLP:journals/corr/abs-2002-06100,
  author    = {Emile van Krieken and
               Erman Acar and
               Frank van Harmelen},
  title     = {Analyzing Differentiable Fuzzy Logic Operators},
  journal   = {CoRR},
  volume    = {abs/2002.06100},
  year      = {2020},
  url       = {https://arxiv.org/abs/2002.06100},
  archivePrefix = {arXiv},
  eprint    = {2002.06100},
  timestamp = {Sat, 23 Jan 2021 01:12:57 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2002-06100.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/kr/KriekenAH20,
  author    = {Emile van Krieken and
               Erman Acar and
               Frank van Harmelen},
  editor    = {Diego Calvanese and
               Esra Erdem and
               Michael Thielscher},
  title     = {Analyzing Differentiable Fuzzy Implications},
  booktitle = {Proceedings of the 17th International Conference on Principles of
               Knowledge Representation and Reasoning, {KR} 2020, Rhodes, Greece,
               September 12-18, 2020},
  pages     = {893--903},
  year      = {2020},
  url       = {https://doi.org/10.24963/kr.2020/92},
  doi       = {10.24963/kr.2020/92},
  timestamp = {Fri, 09 Apr 2021 18:52:15 +0200},
  biburl    = {https://dblp.org/rec/conf/kr/KriekenAH20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/kr/FengASWL20,
  author    = {Renyan Feng and
               Erman Acar and
               Stefan Schlobach and
               Yisong Wang and
               Wanwei Liu},
  editor    = {Diego Calvanese and
               Esra Erdem and
               Michael Thielscher},
  title     = {On Sufficient and Necessary Conditions in Bounded {CTL:} {A} Forgetting
               Approach},
  booktitle = {Proceedings of the 17th International Conference on Principles of
               Knowledge Representation and Reasoning, {KR} 2020, Rhodes, Greece,
               September 12-18, 2020},
  pages     = {361--370},
  year      = {2020},
  url       = {https://doi.org/10.24963/kr.2020/37},
  doi       = {10.24963/kr.2020/37},
  timestamp = {Fri, 09 Apr 2021 18:52:15 +0200},
  biburl    = {https://dblp.org/rec/conf/kr/FengASWL20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{3ecc2712859d48efa2056d0ed7b301aa,
title = "MetaLink: A Travel Guide to the LOD Cloud",
author = "Wouter Beek and Joe Raad and Erman Acar and {van Harmelen}, Frank",
year = "2020",
language = "English",
journal = "Proceedings of the 17th European Semantic Web Conference 2020",
}

@article{a696b1346ffd48b98cd45cee53b69329,
title = "Distance-Based Equilibria in Normal-Form Games",
author = "Erman Acar and Reshef Meir",
year = "2020",
month = "1",
day = "20",
language = "English",
journal = "Proceedings of the AAAI Conference on Artificial Intelligence",
issn = "2159-5399",
}

@article{DBLP:journals/artmed/DeneckeH19,
  author    = {Kerstin Denecke and
               Frank van Harmelen},
  title     = {Recent advances in extracting and processing rich semantics from medical
               texts},
  journal   = {Artif. Intell. Medicine},
  volume    = {93},
  pages     = {11--12},
  year      = {2019},
  url       = {https://doi.org/10.1016/j.artmed.2018.07.004},
  doi       = {10.1016/j.artmed.2018.07.004},
  timestamp = {Thu, 20 Feb 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/artmed/DeneckeH19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/flap/KriekenAH19,
  author    = {Emile van Krieken and
               Erman Acar and
               Frank van Harmelen},
  title     = {Semi-supervised Learning using Differentiable Reasoning},
  journal   = {{FLAP}},
  volume    = {6},
  number    = {4},
  pages     = {633--652},
  year      = {2019},
  url       = {https://collegepublications.co.uk/ifcolog/?00033},
  timestamp = {Fri, 29 Jan 2021 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/flap/KriekenAH19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/jwe/HarmelenT19,
  author    = {Frank van Harmelen and
               Annette ten Teije},
  title     = {A Boxology of Design Patterns forHybrid Learningand Reasoning Systems},
  journal   = {J. Web Eng.},
  volume    = {18},
  number    = {1-3},
  pages     = {97--124},
  year      = {2019},
  url       = {https://doi.org/10.13052/jwe1540-9589.18133},
  doi       = {10.13052/jwe1540-9589.18133},
  timestamp = {Fri, 27 Mar 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/jwe/HarmelenT19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/ws/WilckeBKHS19,
  author    = {Xander Wilcke and
               Victor de Boer and
               M. T. M. de Kleijn and
               Frank van Harmelen and
               Henk J. Scholten},
  title     = {User-centric pattern mining on knowledge graphs: An archaeological
               case study},
  journal   = {J. Web Semant.},
  volume    = {59},
  year      = {2019},
  url       = {https://doi.org/10.1016/j.websem.2018.12.004},
  doi       = {10.1016/j.websem.2018.12.004},
  timestamp = {Fri, 09 Apr 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/ws/WilckeBKHS19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/bnaic/HarmelenT19,
  author    = {Frank van Harmelen and
               Annette ten Teije},
  editor    = {Katrien Beuls and
               Bart Bogaerts and
               Gianluca Bontempi and
               Pierre Geurts and
               Nick Harley and
               Bertrand Lebichot and
               Tom Lenaerts and
               Gilles Louppe and
               Paul Van Eecke},
  title     = {A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems},
  booktitle = {Proceedings of the 31st Benelux Conference on Artificial Intelligence
               {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning
               (Benelearn 2019), Brussels, Belgium, November 6-8, 2019},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2491},
  publisher = {CEUR-WS.org},
  year      = {2019},
  url       = {http://ceur-ws.org/Vol-2491/abstract63.pdf},
  timestamp = {Wed, 12 Feb 2020 16:45:15 +0100},
  biburl    = {https://dblp.org/rec/conf/bnaic/HarmelenT19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/f-egc/Harmelen19,
  author    = {Frank van Harmelen},
  editor    = {Marie{-}Christine Rousset and
               Lydia Boudjeloud{-}Assala},
  title     = {Combining learning and reasoning: new challenges for knowledge graphs},
  booktitle = {Extraction et Gestion des connaissances, {EGC} 2019, Metz, France,
               January 21-25, 2019},
  series    = {{RNTI}},
  volume    = {{E-35}},
  pages     = {1--2},
  publisher = {{\'{E}}ditions {RNTI}},
  year      = {2019},
  url       = {http://editions-rnti.fr/?inprocid=1002459},
  timestamp = {Tue, 26 Nov 2019 17:09:48 +0100},
  biburl    = {https://dblp.org/rec/conf/f-egc/Harmelen19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/kcap/IdrissouZHL19,
  author    = {Al Koudous Idrissou and
               Veruska Zamborlini and
               Frank van Harmelen and
               Chiara Latronico},
  editor    = {Mayank Kejriwal and
               Pedro A. Szekely and
               Rapha{\"{e}}l Troncy},
  title     = {Contextual Entity Disambiguation in Domains with Weak Identity Criteria:
               Disambiguating Golden Age Amsterdamers},
  booktitle = {Proceedings of the 10th International Conference on Knowledge Capture,
               {K-CAP} 2019, Marina Del Rey, CA, USA, November 19-21, 2019},
  pages     = {259--262},
  publisher = {{ACM}},
  year      = {2019},
  url       = {https://doi.org/10.1145/3360901.3364440},
  doi       = {10.1145/3360901.3364440},
  timestamp = {Sat, 30 May 2020 20:04:59 +0200},
  biburl    = {https://dblp.org/rec/conf/kcap/IdrissouZHL19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/semweb/AsprinoBCHP19a,
  author    = {Luigi Asprino and
               Wouter Beek and
               Paolo Ciancarini and
               Frank van Harmelen and
               Valentina Presutti},
  editor    = {Chiara Ghidini and
               Olaf Hartig and
               Maria Maleshkova and
               Vojtech Sv{\'{a}}tek and
               Isabel F. Cruz and
               Aidan Hogan and
               Jie Song and
               Maxime Lefran{\c{c}}ois and
               Fabien Gandon},
  title     = {Observing {LOD} Using Equivalent Set Graphs: It Is Mostly Flat and
               Sparsely Linked},
  booktitle = {The Semantic Web - {ISWC} 2019 - 18th International Semantic Web Conference,
               Auckland, New Zealand, October 26-30, 2019, Proceedings, Part {I}},
  series    = {Lecture Notes in Computer Science},
  volume    = {11778},
  pages     = {57--74},
  publisher = {Springer},
  year      = {2019},
  url       = {https://doi.org/10.1007/978-3-030-30793-6\_4},
  doi       = {10.1007/978-3-030-30793-6\_4},
  timestamp = {Fri, 09 Apr 2021 18:46:52 +0200},
  biburl    = {https://dblp.org/rec/conf/semweb/AsprinoBCHP19a.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/semweb/AsprinoBCHP19,
  author    = {Luigi Asprino and
               Wouter Beek and
               Paolo Ciancarini and
               Frank van Harmelen and
               Valentina Presutti},
  editor    = {Mari Carmen Su{\'{a}}rez{-}Figueroa and
               Gong Cheng and
               Anna Lisa Gentile and
               Christophe Gu{\'{e}}ret and
               C. Maria Keet and
               Abraham Bernstein},
  title     = {Triplifying Equivalence Set Graphs},
  booktitle = {Proceedings of the {ISWC} 2019 Satellite Tracks (Posters {\&}
               Demonstrations, Industry, and Outrageous Ideas) co-located with 18th
               International Semantic Web Conference {(ISWC} 2019), Auckland, New
               Zealand, October 26-30, 2019},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2456},
  pages     = {253--256},
  publisher = {CEUR-WS.org},
  year      = {2019},
  url       = {http://ceur-ws.org/Vol-2456/paper66.pdf},
  timestamp = {Wed, 12 Feb 2020 16:44:52 +0100},
  biburl    = {https://dblp.org/rec/conf/semweb/AsprinoBCHP19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/semweb/PanZSHGZ19,
  author    = {Jeff Z. Pan and
               Mei Zhang and
               Kuldeep Singh and
               Frank van Harmelen and
               Jinguang Gu and
               Zhi Zhang},
  editor    = {Chiara Ghidini and
               Olaf Hartig and
               Maria Maleshkova and
               Vojtech Sv{\'{a}}tek and
               Isabel F. Cruz and
               Aidan Hogan and
               Jie Song and
               Maxime Lefran{\c{c}}ois and
               Fabien Gandon},
  title     = {Entity Enabled Relation Linking},
  booktitle = {The Semantic Web - {ISWC} 2019 - 18th International Semantic Web Conference,
               Auckland, New Zealand, October 26-30, 2019, Proceedings, Part {I}},
  series    = {Lecture Notes in Computer Science},
  volume    = {11778},
  pages     = {523--538},
  publisher = {Springer},
  year      = {2019},
  url       = {https://doi.org/10.1007/978-3-030-30793-6\_30},
  doi       = {10.1007/978-3-030-30793-6\_30},
  timestamp = {Fri, 27 Mar 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/semweb/PanZSHGZ19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/webi/HengstHHB19,
  author    = {Floris den Hengst and
               Mark Hoogendoorn and
               Frank van Harmelen and
               Joost Bosman},
  editor    = {Payam M. Barnaghi and
               Georg Gottlob and
               Yannis Manolopoulos and
               Theodoros Tzouramanis and
               Athena Vakali},
  title     = {Reinforcement Learning for Personalized Dialogue Management},
  booktitle = {2019 {IEEE/WIC/ACM} International Conference on Web Intelligence,
               {WI} 2019, Thessaloniki, Greece, October 14-17, 2019},
  pages     = {59--67},
  publisher = {{ACM}},
  year      = {2019},
  url       = {https://doi.org/10.1145/3350546.3352501},
  doi       = {10.1145/3350546.3352501},
  timestamp = {Thu, 12 Aug 2021 09:49:21 +0200},
  biburl    = {https://dblp.org/rec/conf/webi/HengstHHB19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/aaaiss/2019make,
  editor    = {Andreas Martin and
               Knut Hinkelmann and
               Aurona Gerber and
               Doug Lenat and
               Frank van Harmelen and
               Peter Clark},
  title     = {Proceedings of the {AAAI} 2019 Spring Symposium on Combining Machine
               Learning with Knowledge Engineering {(AAAI-MAKE} 2019) Stanford University,
               Palo Alto, California, USA, March 25-27, 2019., Stanford University,
               Palo Alto, California, USA, March 25-27, 2019},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2350},
  publisher = {CEUR-WS.org},
  year      = {2019},
  url       = {http://ceur-ws.org/Vol-2350},
  urn       = {urn:nbn:de:0074-2350-0},
  timestamp = {Mon, 08 Feb 2021 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/aaaiss/2019make.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/ccks/2018,
  editor    = {Jun Zhao and
               Frank van Harmelen and
               Jie Tang and
               Xianpei Han and
               Quan Wang and
               Xianyong Li},
  title     = {Knowledge Graph and Semantic Computing. Knowledge Computing and Language
               Understanding - Third China Conference, {CCKS} 2018, Tianjin, China,
               August 14-17, 2018, Revised Selected Papers},
  series    = {Communications in Computer and Information Science},
  volume    = {957},
  publisher = {Springer},
  year      = {2019},
  url       = {https://doi.org/10.1007/978-981-13-3146-6},
  doi       = {10.1007/978-981-13-3146-6},
  isbn      = {978-981-13-3145-9},
  timestamp = {Fri, 27 Mar 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/ccks/2018.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1905-12389,
  author    = {Frank van Harmelen and
               Annette ten Teije},
  title     = {A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems},
  journal   = {CoRR},
  volume    = {abs/1905.12389},
  year      = {2019},
  url       = {http://arxiv.org/abs/1905.12389},
  archivePrefix = {arXiv},
  eprint    = {1905.12389},
  timestamp = {Mon, 03 Jun 2019 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1905-12389.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1906-08097,
  author    = {Luigi Asprino and
               Wouter Beek and
               Paolo Ciancarini and
               Frank van Harmelen and
               Valentina Presutti},
  title     = {The Linked Open Data cloud is more abstract, flatter and less linked
               than you may think!},
  journal   = {CoRR},
  volume    = {abs/1906.08097},
  year      = {2019},
  url       = {http://arxiv.org/abs/1906.08097},
  archivePrefix = {arXiv},
  eprint    = {1906.08097},
  timestamp = {Mon, 24 Jun 2019 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1906-08097.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1907-10528,
  author    = {Joe Raad and
               Nathalie Pernelle and
               Fatiha Sa{\"{\i}}s and
               Wouter Beek and
               Frank van Harmelen},
  title     = {The sameAs Problem: {A} Survey on Identity Management in the Web of
               Data},
  journal   = {CoRR},
  volume    = {abs/1907.10528},
  year      = {2019},
  url       = {http://arxiv.org/abs/1907.10528},
  archivePrefix = {arXiv},
  eprint    = {1907.10528},
  timestamp = {Thu, 01 Aug 2019 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1907-10528.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1908-00286,
  author    = {Floris den Hengst and
               Mark Hoogendoorn and
               Frank van Harmelen and
               Joost Bosman},
  title     = {Reinforcement Learning for Personalized Dialogue Management},
  journal   = {CoRR},
  volume    = {abs/1908.00286},
  year      = {2019},
  url       = {http://arxiv.org/abs/1908.00286},
  archivePrefix = {arXiv},
  eprint    = {1908.00286},
  timestamp = {Fri, 22 Nov 2019 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1908-00286.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1908-04700,
  author    = {Emile van Krieken and
               Erman Acar and
               Frank van Harmelen},
  title     = {Semi-Supervised Learning using Differentiable Reasoning},
  journal   = {CoRR},
  volume    = {abs/1908.04700},
  year      = {2019},
  url       = {http://arxiv.org/abs/1908.04700},
  archivePrefix = {arXiv},
  eprint    = {1908.04700},
  timestamp = {Sat, 23 Jan 2021 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1908-04700.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/artmed/RianoPT19,
  author    = {David Ria{\~{n}}o and
               Mor Peleg and
               Annette ten Teije},
  title     = {Ten years of knowledge representation for health care {(2009-2018):}
               Topics, trends, and challenges},
  journal   = {Artif. Intell. Medicine},
  volume    = {100},
  year      = {2019},
  url       = {https://doi.org/10.1016/j.artmed.2019.101713},
  doi       = {10.1016/j.artmed.2019.101713},
  timestamp = {Sat, 30 May 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/artmed/RianoPT19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/jwe/HarmelenT19,
  author    = {Frank van Harmelen and
               Annette ten Teije},
  title     = {A Boxology of Design Patterns forHybrid Learningand Reasoning Systems},
  journal   = {J. Web Eng.},
  volume    = {18},
  number    = {1-3},
  pages     = {97--124},
  year      = {2019},
  url       = {https://doi.org/10.13052/jwe1540-9589.18133},
  doi       = {10.13052/jwe1540-9589.18133},
  timestamp = {Fri, 27 Mar 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/jwe/HarmelenT19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/bnaic/HarmelenT19,
  author    = {Frank van Harmelen and
               Annette ten Teije},
  editor    = {Katrien Beuls and
               Bart Bogaerts and
               Gianluca Bontempi and
               Pierre Geurts and
               Nick Harley and
               Bertrand Lebichot and
               Tom Lenaerts and
               Gilles Louppe and
               Paul Van Eecke},
  title     = {A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems},
  booktitle = {Proceedings of the 31st Benelux Conference on Artificial Intelligence
               {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning
               (Benelearn 2019), Brussels, Belgium, November 6-8, 2019},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2491},
  publisher = {CEUR-WS.org},
  year      = {2019},
  url       = {http://ceur-ws.org/Vol-2491/abstract63.pdf},
  timestamp = {Wed, 12 Feb 2020 16:45:15 +0100},
  biburl    = {https://dblp.org/rec/conf/bnaic/HarmelenT19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/aime/2019,
  editor    = {David Ria{\~{n}}o and
               Szymon Wilk and
               Annette ten Teije},
  title     = {Artificial Intelligence in Medicine - 17th Conference on Artificial
               Intelligence in Medicine, {AIME} 2019, Poznan, Poland, June 26-29,
               2019, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {11526},
  publisher = {Springer},
  year      = {2019},
  url       = {https://doi.org/10.1007/978-3-030-21642-9},
  doi       = {10.1007/978-3-030-21642-9},
  isbn      = {978-3-030-21641-2},
  timestamp = {Sat, 19 Oct 2019 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/aime/2019.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/ijcai/2018aih-s,
  editor    = {Fernando Koch and
               Andrew Koster and
               David Ria{\~{n}}o and
               Sara Montagna and
               Michael Schumacher and
               Annette ten Teije and
               Christian Guttmann and
               Manfred Reichert and
               Isabelle Bichindaritz and
               Pau Herrero and
               Richard Lenz and
               Beatriz L{\'{o}}pez and
               Cindy Marling and
               Clare Martin and
               Stefania Montani and
               Nirmalie Wiratunga},
  title     = {Artificial Intelligence in Health - First International Workshop,
               AIH@IJCAI 2018, Stockholm, Sweden, July 13-14, 2018, Revised Selected
               Papers},
  series    = {Lecture Notes in Computer Science},
  volume    = {11326},
  publisher = {Springer},
  year      = {2019},
  url       = {https://doi.org/10.1007/978-3-030-12738-1},
  doi       = {10.1007/978-3-030-12738-1},
  isbn      = {978-3-030-12737-4},
  timestamp = {Fri, 27 Mar 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/ijcai/2018aih-s.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1905-12389,
  author    = {Frank van Harmelen and
               Annette ten Teije},
  title     = {A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems},
  journal   = {CoRR},
  volume    = {abs/1905.12389},
  year      = {2019},
  url       = {http://arxiv.org/abs/1905.12389},
  archivePrefix = {arXiv},
  eprint    = {1905.12389},
  timestamp = {Mon, 03 Jun 2019 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1905-12389.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/compsec/XuLSHL19,
  author    = {Zhou Xu and
               Yaping Lin and
               Voundi Koe Arthur Sandor and
               Zhisheng Huang and
               Xinbo Liu},
  title     = {A lightweight privacy and integrity preserving range query scheme
               for mobile cloud computing},
  journal   = {Comput. Secur.},
  volume    = {84},
  pages     = {318--333},
  year      = {2019},
  url       = {https://doi.org/10.1016/j.cose.2019.04.003},
  doi       = {10.1016/j.cose.2019.04.003},
  timestamp = {Thu, 20 Feb 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/compsec/XuLSHL19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/hisas/ZhongH19,
  author    = {Han Zhong and
               Zhisheng Huang},
  title     = {Document recommendation based on interests of co-authors for brain
               science},
  journal   = {Health Inf. Sci. Syst.},
  volume    = {7},
  number    = {1},
  pages     = {25},
  year      = {2019},
  url       = {https://doi.org/10.1007/s13755-019-0088-y},
  doi       = {10.1007/s13755-019-0088-y},
  timestamp = {Sat, 30 May 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/hisas/ZhongH19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/brain/LinZH19,
  author    = {Shaofu Lin and
               Yuanyuan Zhao and
               Zhisheng Huang},
  editor    = {Peipeng Liang and
               Vinod Goel and
               Chunlei Shan},
  title     = {Time Recognition of Chinese Electronic Medical Record of Depression
               Based on Conditional Random Field},
  booktitle = {Brain Informatics - 12th International Conference, {BI} 2019, Haikou,
               China, December 13-15, 2019, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {11976},
  pages     = {149--158},
  publisher = {Springer},
  year      = {2019},
  url       = {https://doi.org/10.1007/978-3-030-37078-7\_15},
  doi       = {10.1007/978-3-030-37078-7\_15},
  timestamp = {Wed, 11 Dec 2019 16:30:35 +0100},
  biburl    = {https://dblp.org/rec/conf/brain/LinZH19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/his2/QiLH19,
  author    = {Yunfei Qi and
               Shaofu Lin and
               Zhisheng Huang},
  editor    = {Hua Wang and
               Siuly Siuly and
               Rui Zhou and
               Fernando Mart{\'{\i}}n{-}S{\'{a}}nchez and
               Yanchun Zhang and
               Zhisheng Huang},
  title     = {Classification of Skin Pigmented Lesions Based on Deep Residual Network},
  booktitle = {Health Information Science - 8th International Conference, {HIS} 2019,
               Xi'an, China, October 18-20, 2019, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {11837},
  pages     = {58--67},
  publisher = {Springer},
  year      = {2019},
  url       = {https://doi.org/10.1007/978-3-030-32962-4\_6},
  doi       = {10.1007/978-3-030-32962-4\_6},
  timestamp = {Tue, 29 Dec 2020 18:35:52 +0100},
  biburl    = {https://dblp.org/rec/conf/his2/QiLH19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/his2/ZhongH19,
  author    = {Han Zhong and
               Zhisheng Huang},
  editor    = {Hua Wang and
               Siuly Siuly and
               Rui Zhou and
               Fernando Mart{\'{\i}}n{-}S{\'{a}}nchez and
               Yanchun Zhang and
               Zhisheng Huang},
  title     = {Document Recommendation Based on Interests of Co-authors for Brain
               Science},
  booktitle = {Health Information Science - 8th International Conference, {HIS} 2019,
               Xi'an, China, October 18-20, 2019, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {11837},
  pages     = {108--118},
  publisher = {Springer},
  year      = {2019},
  url       = {https://doi.org/10.1007/978-3-030-32962-4\_11},
  doi       = {10.1007/978-3-030-32962-4\_11},
  timestamp = {Thu, 10 Oct 2019 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/his2/ZhongH19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/his2/LiuH19,
  author    = {Ting Liu and
               Zhisheng Huang},
  editor    = {Hua Wang and
               Siuly Siuly and
               Rui Zhou and
               Fernando Mart{\'{\i}}n{-}S{\'{a}}nchez and
               Yanchun Zhang and
               Zhisheng Huang},
  title     = {Evidence-Based Analysis of Neurotransmitter Modulation by Gut Microbiota},
  booktitle = {Health Information Science - 8th International Conference, {HIS} 2019,
               Xi'an, China, October 18-20, 2019, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {11837},
  pages     = {238--249},
  publisher = {Springer},
  year      = {2019},
  url       = {https://doi.org/10.1007/978-3-030-32962-4\_22},
  doi       = {10.1007/978-3-030-32962-4\_22},
  timestamp = {Thu, 10 Oct 2019 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/his2/LiuH19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/his2/HuangHWZYW19,
  author    = {Zhisheng Huang and
               Qing Hu and
               Haiyuan Wang and
               Yahan Zhang and
               Jie Yang and
               Gang Wang},
  editor    = {Hua Wang and
               Siuly Siuly and
               Rui Zhou and
               Fernando Mart{\'{\i}}n{-}S{\'{a}}nchez and
               Yanchun Zhang and
               Zhisheng Huang},
  title     = {Semantic Processing of Personality Description for Depression Patients},
  booktitle = {Health Information Science - 8th International Conference, {HIS} 2019,
               Xi'an, China, October 18-20, 2019, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {11837},
  pages     = {263--275},
  publisher = {Springer},
  year      = {2019},
  url       = {https://doi.org/10.1007/978-3-030-32962-4\_24},
  doi       = {10.1007/978-3-030-32962-4\_24},
  timestamp = {Thu, 10 Oct 2019 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/his2/HuangHWZYW19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/his2/2019,
  editor    = {Hua Wang and
               Siuly Siuly and
               Rui Zhou and
               Fernando Mart{\'{\i}}n{-}S{\'{a}}nchez and
               Yanchun Zhang and
               Zhisheng Huang},
  title     = {Health Information Science - 8th International Conference, {HIS} 2019,
               Xi'an, China, October 18-20, 2019, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {11837},
  publisher = {Springer},
  year      = {2019},
  url       = {https://doi.org/10.1007/978-3-030-32962-4},
  doi       = {10.1007/978-3-030-32962-4},
  isbn      = {978-3-030-32961-7},
  timestamp = {Tue, 29 Dec 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/his2/2019.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DataIntelligence2019,
title = "Constructing and Cleaning Identity Graphs in the LOD Cloud",
author = "Joe Raad and Wouter Beek and Frank van Harmelen and Jan Wielemaker And Nathalie Pernelle and Fatiha Sais",
journal = "Data Intelligence",
year = "2019",
volume = "xxx",
number = "xxx",
pages = "xxx",
urlPaper = "http://www.cs.vu.nl/~frankh/postscript/DataIntelligence2019.pdf",
keywords = {Semantic Web}
}

@InProceedings{WI2019,
  author =       "Floris den Hengst and Mark Hoogendoorn and Frank van Harmelen and Joost Bosman",
  title =        "Reinforcement Learning for Personalized Dialogue Management",
  booktitle =    "IEEE/WIC/ACM International Conference on Web Intelligence
                  ({WI '19})",
  year =         2019,
  editor =       "xxx",
  number =       "xxx",
  pages =        "xxx",
  publisher =    "IEEE",
  isbn =         "978-1-4503-6934-3/19/10",
  doi =          "http://dx.doi.org/10.1145/3350546.3352501",
  keywords = {Machine Learning},
  urlPaper = "http://www.cs.vu.nl/~frankh/postscript/WI2019.pdf"
}

@InProceedings{ISWC2019-RelationLinker,
  author    = {Jeff Z. Pan and Mei Zhang and Kuldeep Singh and
               Frank van Harmelen  and Jinguang Gu and Zhi Zhang},
  title     = {Entity Enabled Relation Linking},
  booktitle = "Proceedings of the 18th Internation Semantic Web Conference",
  year =      "2019",
  pages =     "xxx-xxx",
  editor    = {xxx},
  publisher = "Springer Verlag",
  series =    "Lecture Notes in Computer Science",
  number =    "xxx",
  keywords = {Semantic Web},
  urlPaper = "http://www.cs.vu.nl/~frankh/postscript/ISWC2019-RelationLinker.pdf"
}

@InProceedings{ISWC2019-LODanalytics,
  author    = {Luigi Asprino and Wouter Beek and Paolo Ciancarini and Frank van Harmelen and Valentina Presutti},
  title     = {Observing the LOD Cloud using Equivalent Set Graphs:
               the LOD Cloud is mostly flat and sparsely linked},
  booktitle = "Proceedings of the 18th Internation Semantic Web Conference",
  year =      "2019",
  pages =     "xxx-xxx",
  editor    = {xxx},
  publisher = "Springer Verlag",
  series =    "Lecture Notes in Computer Science",
  number =    "xxx",
  keywords = {Semantic Web},
  urlPaper = "http://www.cs.vu.nl/~frankh/postscript/ISWC2019-LODanalytics.pdf"
}

@article{JWE2019,
title = "A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems",
author = "Frank van Harmelen and Annette ten Teije",
journal = "Journal of Web Engineering",
year = "2019",
volume = "18",
number = "1-3",
pages = "97-124",
doi = " 10.13052/jwe1540-9589.18133",
urlPaper = "http://www.cs.vu.nl/~frankh/postscript/JWE2019.pdf",
keywords = {Knowledge Representation},
keywords = {Machine Learning},
abstract = "We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature."
}

We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature.

@article{JWS2019,
title = "User-centric pattern mining on knowledge graphs: An archaeological case study",
author = "W.X. Wilcke and V. de Boer and M.T.M. de Kleijn and F.A.H. van Harmelen and H.J. Scholten",
journal = "Journal of Web Semantics",
year = "2019",
volume = {in print},
issn = "1570-8268",
doi = "10.1016/j.websem.2018.12.004",
url = "http://www.sciencedirect.com/science/article/pii/S1570826818300660",
urlPaper = "http://www.cs.vu.nl/~frankh/postscript/JWS2019.pdf",
keywords = {Semantic Web},
abstract = "In recent years, there has been a growing interest from the digital humanities in knowledge graphs as data modelling paradigm. Already, this has led to the creation of many such knowledge graphs, many of which are now available as part of the Linked Open Data cloud. This presents new opportunities for data mining. In this work, we develop, implement, and evaluate (both data-driven and user-driven) an end-to-end pipeline for user-centric pattern mining on knowledge graphs in the humanities. This pipeline combines constrained generalized association rule mining with natural language output and facet rule browsing to allow for transparency and interpretability?two key domain requirements. Experiments in the archaeological domain show that domain experts were positively surprised by the range of patterns that were discovered and were overall optimistic about the future potential of this approach."
}

In recent years, there has been a growing interest from the digital humanities in knowledge graphs as data modelling paradigm. Already, this has led to the creation of many such knowledge graphs, many of which are now available as part of the Linked Open Data cloud. This presents new opportunities for data mining. In this work, we develop, implement, and evaluate (both data-driven and user-driven) an end-to-end pipeline for user-centric pattern mining on knowledge graphs in the humanities. This pipeline combines constrained generalized association rule mining with natural language output and facet rule browsing to allow for transparency and interpretability?two key domain requirements. Experiments in the archaeological domain show that domain experts were positively surprised by the range of patterns that were discovered and were overall optimistic about the future potential of this approach.

@Article{JAL2019,
  author = 	 {Emile van Krieken and Erman Acar and Frank van Harmelen},
  title = 	 {Semi-Supervised Learning using Differentiable Reasoning},
  journal = 	 {Journal of Applied Logics},
  year = 	 2019,
  volume = 	 {in print},
  keywords =     {Knowledge Representation},
  urlPaper = "http://www.cs.vu.nl/~frankh/postscript/JAL2019.pdf"
}

@article{raad2019sameas,
  title={The sameAs Problem: A Survey on Identity Management in the Web of Data},
  author={Raad, Joe and Pernelle, Nathalie and Sa{\"\i}s, Fatiha and Beek, Wouter and van Harmelen, Frank},
  journal={arXiv preprint arXiv:1907.10528},
  year={2019}
}

@article{asprino2019linked,
  title={The Linked Open Data cloud is more abstract, flatter and less linked than you may think!},
  author={Asprino, Luigi and Beek, Wouter and Ciancarini, Paolo and van Harmelen, Frank and Presutti, Valentina},
  journal={arXiv preprint arXiv:1906.08097},
  year={2019}
}

@inproceedings{folmer2019enhancing,
  title={Enhancing the Usefulness of Open Governmental Data with Linked Data Viewing Techniques},
  author={Folmer, Erwin and Beek, Wouter and Rietveld, Laurens and Ronzhin, Stanislav and Geerling, Rutger and den Haan, Davey},
  booktitle={Proceedings of the 52nd Hawaii International Conference on System Sciences},
  year={2019}
}

@inproceedings{daga2019modelling,
  title={Modelling and Querying Lists in RDF. A Pragmatic Study},
  author={Daga, Enrico and Mero{\~n}o-Pe{\~n}uela, Albert and Motta, Enrico},
  booktitle={ISWC Workshops: QuWeDa},
  pages={In--Press},
  year={2019}
}

@article{merono2019list,
  title={List. MID: A MIDI-Based Benchmark for Evaluating RDF Lists},
  author={Mero{\~n}o-Pe{\~n}uela, Albert and Daga, Enrico},
  journal={Lecture Notes in Computer Science},
  pages={In--Press},
  year={2019},
  publisher={Springer}
}

@inproceedings{acar2019making,
  title={Making Decisions over Contextual Ontologies},
  author={Acar, E and Penaloza, Rafael and Prediou, Livia},
  booktitle={Description Logics 2019 Workshop Proceedings},
  year={2019}
}

@inproceedings{acar2019satisfiability,
  title={Satisfiability in Strategy Logic can be Easier than Model Checking},
  author={Acar, E and Benerecetti, Massimo and Mogavero, Fabio},
  booktitle={Proceedings of AAAI Conference on Artificial Intelligence 2019},
  year={2019}
}

@article{van2019differentiable,
  title={Differentiable Fuzzy Logics},
  author={van Krieken, Emile},
  year={2019}
}

@inproceedings{chiatti2019exploring,
  title={Exploring task-agnostic, ShapeNet-based object recognition for mobile robots},
  author={Chiatti, Agnese and Bardaro, Gianluca and Bastianelli, Emanuele and Tiddi, Ilaria and Mitra, Prasenjit and Motta, Enrico},
  booktitle={2019 Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2019},
  pages={1--8},
  year={2019},
  organization={CEUR Workshop Proceedings}
}

@article{DBLP:journals/corr/abs-1907-10528,
  author    = {Joe Raad and
               Nathalie Pernelle and
               Fatiha Sa{\"{\i}}s and
               Wouter Beek and
               Frank van Harmelen},
  title     = {The sameAs Problem: {A} Survey on Identity Management in the Web of
               Data},
  journal   = {CoRR},
  volume    = {abs/1907.10528},
  year      = {2019},
  url       = {http://arxiv.org/abs/1907.10528},
  archivePrefix = {arXiv},
  eprint    = {1907.10528},
  timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1907-10528},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/access/HuangYLXL18,
  author    = {Zhisheng Huang and
               Xiai Yan and
               Yaping Lin and
               Zhou Xu and
               Feng Lin},
  title     = {A Secure and Efficient Privacy-Preserving Range Query Scheme in Location-Based
               Services},
  journal   = {{IEEE} Access},
  volume    = {6},
  pages     = {72796--72807},
  year      = {2018},
  url       = {https://doi.org/10.1109/ACCESS.2018.2882399},
  doi       = {10.1109/ACCESS.2018.2882399},
  timestamp = {Wed, 26 Dec 2018 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/access/HuangYLXL18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/his2/ZhaoLH18,
  author    = {Xiaoli Zhao and
               Shaofu Lin and
               Zhisheng Huang},
  editor    = {Siuly Siuly and
               Ickjai Lee and
               Zhisheng Huang and
               Rui Zhou and
               Hua Wang and
               Wei Xiang},
  title     = {Extraction of Semantic Relations from Medical Literature Based on
               Semantic Predicates and {SVM}},
  booktitle = {Health Information Science - 7th International Conference, {HIS} 2018,
               Cairns, QLD, Australia, October 5-7, 2018, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {11148},
  pages     = {17--24},
  publisher = {Springer},
  year      = {2018},
  url       = {https://doi.org/10.1007/978-3-030-01078-2\_2},
  doi       = {10.1007/978-3-030-01078-2\_2},
  timestamp = {Wed, 14 Apr 2021 16:50:07 +0200},
  biburl    = {https://dblp.org/rec/conf/his2/ZhaoLH18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/his2/2018,
  editor    = {Siuly Siuly and
               Ickjai Lee and
               Zhisheng Huang and
               Rui Zhou and
               Hua Wang and
               Wei Xiang},
  title     = {Health Information Science - 7th International Conference, {HIS} 2018,
               Cairns, QLD, Australia, October 5-7, 2018, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {11148},
  publisher = {Springer},
  year      = {2018},
  url       = {https://doi.org/10.1007/978-3-030-01078-2},
  doi       = {10.1007/978-3-030-01078-2},
  isbn      = {978-3-030-01077-5},
  timestamp = {Wed, 14 Apr 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/his2/2018.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{7fcc8392689e4c6f8bc5c8ae55eb89a2,
  title     = "Literally better: Analyzing and improving the quality of literals",
  abstract  = "Quality is a complicated and multifarious topic in contemporary Linked Data research. The aspect of literal quality in particular has not yet been rigorously studied. Nevertheless, analyzing and improving the quality of literals is important since literals form a substantial (one in seven statements) and crucial part of the Semantic Web. Specifically, literals allow infinite value spaces to be expressed and they provide the linguistic entry point to the LOD Cloud. We present a toolchain that builds on the LOD Laundromat data cleaning and republishing infrastructure and that allows us to analyze the quality of literals on a very large scale, using a collection of quality criteria we specify in a systematic way. We illustrate the viability of our approach by lifting out two particular aspects in which the current LOD Cloud can be immediately improved by automated means: value canonization and language tagging. Since not all quality aspects can be addressed algorithmically, we also give an overview of other problems that can be used to guide future endeavors in tooling, training, and best practice formulation.",
  keywords  = "data observatory, Data quality, linked data, quality assessment, quality improvement",
  author    = "Wouter Beek and Filip Ilievski and Jeremy Debattista and Stefan Schlobach and Jan Wielemaker",
  year      = "2018",
  doi       = "10.3233/SW-170288",
  volume    = "9",
  pages     = "131--150",
  journal   = "Semantic Web",
  issn      = "1570-0844",
  publisher = "IOS Press",
  number    = "1",
}

Quality is a complicated and multifarious topic in contemporary Linked Data research. The aspect of literal quality in particular has not yet been rigorously studied. Nevertheless, analyzing and improving the quality of literals is important since literals form a substantial (one in seven statements) and crucial part of the Semantic Web. Specifically, literals allow infinite value spaces to be expressed and they provide the linguistic entry point to the LOD Cloud. We present a toolchain that builds on the LOD Laundromat data cleaning and republishing infrastructure and that allows us to analyze the quality of literals on a very large scale, using a collection of quality criteria we specify in a systematic way. We illustrate the viability of our approach by lifting out two particular aspects in which the current LOD Cloud can be immediately improved by automated means: value canonization and language tagging. Since not all quality aspects can be addressed algorithmically, we also give an overview of other problems that can be used to guide future endeavors in tooling, training, and best practice formulation.

@article{AIMed2018,
author = "Kerstin Denecke and Frank van Harmelen",
title = "Recent advances in extracting and processing rich semantics from medical texts",
journal = "Artificial Intelligence in Medicine",
year = "2018",
issn = "0933-3657",
doi = "https://doi.org/10.1016/j.artmed.2018.07.004",
urlPaper = "http://www.cs.vu.nl/~frankh/postscript/AIMed2018.pdf",
keywords = {Medical Knowledge Representation}
}

@InProceedings{ISWC2018,
  author    = {Joe Raad and Wouter Beek and Frank van Harmelen and
               Nathalie Pernelle and Fatiha Sa{\"{\i}}s},
  title     = {Detecting Erroneous Identity Links on the Web Using Network Metrics},
  booktitle = "Proceedings of the 17th International Semantic Web Conference",
  year =      "2018",
  pages =     "391--407--68",
  editor    = {Denny Vrandecic and
               Kalina Bontcheva and
               Mari Carmen Su{\'{a}}rez{-}Figueroa and
               Valentina Presutti and
               Irene Celino and
               Marta Sabou and
               Lucie{-}Aim{\'{e}}e Kaffee and
               Elena Simperl},
  publisher = "Springer Verlag",
  series =    "Lecture Notes in Computer Science",
  number =    "11136",
  keywords = {Semantic Web},
  urlPaper = "http://www.cs.vu.nl/~frankh/postscript/ISWC2018.pdf"
}

@InProceedings{EKAW2018,
  author={Al Koudous Idrissou and Frank van Harmelen and
         Peter Van den Besselaar},
  title = {Network Metrics for Assessing the Quality of Entity Resolution
           Between Multiple Datasets},
  booktitle={21st International Conference on Knowledge Engineering and
             Knowledge Management, {EKAW} 2018},
  editor={Catherine Faron{-}Zucker and Chiara Ghidini and
               Amedeo Napoli and Yannick Toussaint},
  year = 2018,
  volume = 11313,
  isbn={978-3-030-03666-9},
  pages={147--162},
  doi       = {10.1007/978-3-030-03667-6\_10},
  series = {LNCS},
  publisher={Springer International Publishing},
  keywords = {Medical Knowledge Representation},
  urlPaper= "http://www.cs.vu.nl/~frankh/postscript/EKAW2018.pdf"
}

@inproceedings{ESWC2018,
  title={sameAs.cc: The Closure of 500M owl:sameAs Statements},
  author={Wouter Beek and Joe Raad and Jan Wielemaker and Frank van Harmelen},
  booktitle={European Semantic Web Conference, ESWC 2018},
  pages={},
  year={2018},
  publisher={Springer Berlin Heidelberg},
  keywords = {Semantic Web},
  urlPaper = "http://www.cs.vu.nl/~frankh/postscript/ESWC2018.pdf"
}

@article{avila2018classifying,
  title={CLASSIFYING THE LOD CLOUD},
  author={{\'A}vila, Daniel Mart{\'\i}nez and Smiraglia, Richard P and Szostak, Rick and Scharnhorst, Andrea and Beek, Wouter and Siebes, Ronald and Ridenour, Laura and Schlais, Vanessa},
  journal={Brazilian Journal of Information Science: research trends},
  volume={12},
  number={4},
  pages={06--10},
  year={2018}
}

@article{folmer2018linked,
  title={Linked Data Viewing as part of the Spatial Data Platform of the Future},
  author={Folmer, E and Beek, W and Rietveld, Luuk},
  journal={International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
  volume={42},
  number={4},
  pages={49--52},
  year={2018},
  publisher={Copernicus Publications}
}

@article{beek2018adding,
  title={Adding 3d GIS visualization and navigation to the sparqlquery loop},
  author={Beek, W and Folmer, E and Rietveld, L and Baving, T and Van Altena, V},
  journal={International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences-ISPRS Archives},
  volume={42},
  number={4/W8},
  pages={11--14},
  year={2018}
}

@article{beek2018k,
  title={The'K'in'Semantic Web'Stands for'Knowledge'},
  author={Beek, WGJ},
  year={2018},
  publisher={Amsterdam: Vrije Universiteit}
}

@inproceedings{beek2018nlgis,
  title={nlGis: A use case in linked historic geodata},
  author={Beek, Wouter and Zijdeman, Richard},
  booktitle={European Semantic Web Conference},
  pages={437--447},
  year={2018},
  organization={Springer}
}

@article{szostak2018connecting,
  title={Connecting KOSs and the LOD Cloud},
  author={Szostak, Rick and Scharnhorst, Andrea and Beek, Wouter and Smiraglia, Richard P},
  journal={arXiv preprint arXiv:1802.08141},
  year={2018}
}

@article{raad2018detection,
  title={D{\'e}tection de liens d?identit{\'e} erron{\'e}s en utilisant la d{\'e}tection de communaut{\'e}s dans les graphes d?identit{\'e}},
  author={Raad, Joe and Beek, Wouter and Pernelle, Nathalie and Sa{\"\i}s, Fatiha and Van Harmelen, Frank},
  journal={ISI},
  volume={23},
  number={3-4},
  year={2018}
}

@inproceedings{merono2018semantic,
  title={The semantic web MIDI tape: An interface for interlinking MIDI and context metadata},
  author={Mero{\~n}o-Pe{\~n}uela, Albert and De Valk, Reinier and Daga, Enrico and Daquino, Marilena and Kent-Muller, Anna},
  booktitle={Proceedings of the 1st International Workshop on Semantic Applications for Audio and Music},
  pages={24--32},
  year={2018},
  organization={ACM}
}

@article{hoekstra2018datalegend,
  title={The dataLegend ecosystem for historical statistics},
  author={Hoekstra, Rinke and Mero{\~n}o-Pe{\~n}uela, Albert and Rijpma, Auke and Zijdeman, Richard and Ashkpour, Ashkan and Dentler, Kathrin and Zandhuis, Ivo and Rietveld, Laurens},
  journal={Journal of Web Semantics},
  volume={50},
  pages={49--61},
  year={2018},
  publisher={Elsevier}
}

@article{merono2018improving,
  title={Improving Access to the Dutch Historical Censuses with Linked Open Data: Social and Economic History},
  author={Mero{\~n}o-Pe{\~n}uela, Albert and Ashkpour, Ashkan and Gilissen, Valentijn and Jonker, Jan and Vreugdenhil, Tom and Doorn, Peter},
  journal={Research Data Journal for the Humanities and Social Sciences},
  volume={3},
  number={1},
  pages={13--26},
  year={2018},
  publisher={Brill}
}

@article{fensel2018joint,
  title={Joint Proceedings of SEMANTiCS 2017 Workshops co-located with the 13th International Conference on Semantic Systems (SEMANTiCS 2017), Amsterdam, Netherlands, September 11 and 14, 2017},
  author={Fensel, Anna and Daniele, Laura and Aroyo, Lora and de Boer, Victor and Dar{\'a}nyi, S{\'a}ndor and Elloumi, Omar and Garc{\'\i}a-Castro, Ra{\'u}l and Hollink, Laura and Inel, Oana and Kuys, Gerard and others},
  year={2018},
  publisher={CEUR-WS. org}
}

@inproceedings{zhao2018text,
  title={Text Classification of Micro-blog's Tree Hole Based on Convolutional Neural Network},
  author={Zhao, Xiaoli and Lin, Shaofu and Huang, Zhisheng},
  booktitle={Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence},
  pages={61},
  year={2018},
  organization={ACM}
}

@inproceedings{zhao2018extraction,
  title={Extraction of semantic relations from medical literature based on semantic predicates and SVM},
  author={Zhao, Xiaoli and Lin, Shaofu and Huang, Zhisheng},
  booktitle={International Conference on Health Information Science},
  pages={17--24},
  year={2018},
  organization={Springer}
}

@inproceedings{du2018making,
  title={Making semantic annotation on patient data of depression},
  author={Du, Yanan and Lin, Shaofu and Huang, Zhisheng},
  booktitle={2nd International Conference on Medical and Health Informatics, ICMHI 2018},
  pages={134--137},
  year={2018},
  organization={Association for Computing Machinery}
}

@misc{teije2018preface,
  title={Preface: AIME 2017.},
  author={Teije, AT and Popow, Christian and Holmes, John H and Sacchi, Lucia},
  year={2018}
}

@conference{b56ce393e04c4f3d8a1c0c70927c9e5c,
title = "Filtering clinical guideline interactions with pre-conditions: A case study on diabetes guideline",
author = "Veruska Zamborlini and {van der Heijden}, Roelof and {ten Teije}, Annette",
year = "2018",
month = "1",
day = "1",
language = "English",
note = "2018 Joint Reasoning with Ambiguous and Conflicting Evidence and Recommendations in Medicine and the 3rd International Workshop on Ontology Modularity, Contextuality, and Evolution, MedRACER + WOMoCoE 2018 ; Conference date: 29-10-2018",
}

@inproceedings{ilievski2018systematic,
  title={Systematic Study of Long Tail Phenomena in Entity Linking},
  author={Ilievski, Filip and Vossen, Piek and Schlobach, Stefan},
  booktitle={Proceedings of the 27th International Conference on Computational Linguistics},
  pages={664--674},
  year={2018}
}

@inproceedings{kiesel2018preferential,
  title={Preferential default reasoning on the semantic web},
  author={Kiesel, Rafael and Acar, Erman and Schlobach, Stefan},
  booktitle={31st International Workshop on Description Logics, DL 2018},
  pages={1--12},
  year={2018}
}

@article{van2018three,
  title={Three Tools for Practical Differential Privacy},
  author={van der Veen, Koen Lennart and Seggers, Ruben and Bloem, Peter and Patrini, Giorgio},
  journal={arXiv preprint arXiv:1812.02890},
  year={2018}
}

@article{bloem2018tutorial,
  title={A tutorial on MDL hypothesis testing for graph analysis},
  author={Bloem, Peter and de Rooij, Steven},
  journal={arXiv preprint arXiv:1810.13163},
  year={2018}
}

@article{bloem2018learning,
  title={Learning sparse transformations through backpropagation},
  author={Bloem, Peter},
  journal={arXiv preprint arXiv:1810.09184},
  year={2018}
}

@article{veer2018deep,
  title={Deep Learning for Classification Tasks on Geospatial Vector Polygons},
  author={Veer, Rein van't and Bloem, Peter and Folmer, Erwin},
  journal={arXiv preprint arXiv:1806.03857},
  year={2018}
}

@inproceedings{schlichtkrull2018modeling,
  title={Modeling relational data with graph convolutional networks},
  author={Schlichtkrull, Michael and Kipf, Thomas N and Bloem, Peter and Van Den Berg, Rianne and Titov, Ivan and Welling, Max},
  booktitle={European Semantic Web Conference},
  pages={593--607},
  year={2018},
  organization={Springer}
}

@article{acar2018knowledge,
  title={Knowledge Representation for Automated Decision Making},
  author={Acar, E},
  year={2018},
  publisher={Mannheim University Library}
}

@article{mensio2018multi,
  title={A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling},
  author={Mensio, Martino and Bastianelli, Emanuele and Tiddi, Ilaria and Rizzo, Giuseppe},
  journal={arXiv preprint arXiv:1811.05242},
  year={2018}
}

@inproceedings{troullinou2018re,
  title={Re-coding Black Mirror Chairs' Welcome \& Organization},
  author={Troullinou, Pinelopi and d'Aquin, Mathieu and Tiddi, Ilaria},
  booktitle={Companion Proceedings of the The Web Conference 2018},
  pages={1527--1528},
  year={2018},
  organization={International World Wide Web Conferences Steering Committee}
}

@inproceedings{bastianelli2018meet,
  title={Meet HanS, the Health\&Safety autonomous inspector},
  author={Bastianelli, Emanuele and Bardaro, Gianluca and Tiddi, Ilaria and Motta, Enrico},
  booktitle={2018 ISWC Posters and Demonstrations, Industry and Blue Sky Ideas Tracks, ISWC-P and D-Industry-BlueSky 2018},
  year={2018},
  organization={CEUR Workshop Proceedings}
}

@inproceedings{tiddi2018user,
  title={A user-friendly interface to control ROS robotic platforms},
  author={Tiddi, Ilaria and Bastianelli, Emanuele and Bardaro, Gianluca and Motta, Enrico},
  booktitle={2018 ISWC Posters and Demonstrations, Industry and Blue Sky Ideas Tracks, ISWC-P and D-Industry-BlueSky 2018},
  year={2018},
  organization={CEUR}
}

@inproceedings{daquino2018creating,
  title={Creating open citation data with BCITE},
  author={Daquino, Marilena and Tiddi, Ilaria and Peroni, Silvio and Shotton, David},
  booktitle={2nd Workshop on Enabling Open Semantic Science, SemSci 2018},
  pages={1--11},
  year={2018},
  organization={CEUR-WS. org}
}

@book{48dc6c68586e4478989885f8a40a478a,
title = "Creating an organization-dataset through entity linking: RISIS Deliverable 8.3",
author = "O.A.K. Idrissou and A. Khalili and {van den Besselaar}, P.A.A. and {van Harmelen}, Frank",
year = "2018",
month = "6",
language = "English",
publisher = "Vrije Universtiteit",
}

@book{3e007352e5b4412dbfc46d255ba899cb,
title = "Interfacing with external heterogeneous data: RISIS Deliverable 7.4",
author = "A. Khalili and {van den Besselaar}, P.A.A. and {de Graaf}, K.A. and O.A.K. Idrissou and {van Harmelen}, Frank",
year = "2018",
language = "English",
publisher = "Vrije Universtiteit",
}

@inbook{ae824c9530eb43e2934e6c5ddcc57acf,
title = "Network metrics for assessing the quality of entity resolution between multiple datasets",
keywords = "Network metrics, Data Integration, Entity resolution",
author = "O.A.K. Idrissou and {van Harmelen}, Frank and {van den Besselaar}, P.A.A.",
year = "2018",
doi = "10.1007/978-3-030-03667-6_10",
language = "English",
isbn = "978-3-030-03666-9",
volume = "11313",
series = "Lecture Notes in Arti?cial Intelligence",
publisher = "Springer Nature Switzerland AG",
pages = "147--162",
editor = "{Faron Zucker}, Catherine and Chiara Ghidini and Amedeo Napoli and Yannick Toussaint",
booktitle = "Knowledge Engineering and Knowledge Management",
}

@book{3543c742576444209797c642013fae03,
title = "The Lenticular Lens: Customized linking and link modification (the SMS disambiguation service): RISIS Deliverable 25-4: Disambiguation methods",
author = "O.A.K. Idrissou and A. Khalili and R.J. Hoekstra and {Carretta Zamborlini}, V. and {van Harmelen}, Frank and {van den Besselaar}, P.A.A.",
year = "2018",
month = "6",
language = "English",
publisher = "Vrije Universtiteit",
}

@conference{590c1a2aa47946c7af309e2ef6917d7d,
title = "ReferenceNet: A semantic-pragmatic network for capturing reference relations",
author = "Piek Vossen and Marten Postma and Filip Ilievski",
year = "2018",
month = "1",
language = "English",
note = "9th Global WordNet Conference, GWC 2018 ; Conference date: 08-01-2018 Through 12-01-2018",
}

@inproceedings{4ad527e286804cd1aeef15921662a614,
title = "Don't Annotate, but Validate: a Data-to-Text Method for Capturing Event Data",
keywords = "Event coreference, Structured data, Text corpora",
author = "Piek Vossen and Filip Ilievski and Marten Postma and R.H. Segers",
year = "2018",
language = "English",
pages = "3034--3042",
editor = "Hitoshi Isahara and Bente Maegaard and Stelios Piperidis and Christopher Cieri and Thierry Declerck and Koiti Hasida and Helene Mazo and Khalid Choukri and Sara Goggi and Joseph Mariani and Asuncion Moreno and Nicoletta Calzolari and Jan Odijk and Takenobu Tokunaga",
booktitle = "[Proceedings of the] Eleventh International Conference on Language Resources and Evaluation (LREC 2018)",
publisher = "LREC",
}

@inproceedings{DBLP:conf/ectel/EliasJRSGKWLA18,
  author    = {Mirette Elias and
               Abi James and
               Edna Ruckhaus and
               Mari Carmen Su{\'{a}}rez{-}Figueroa and
               Klaas Andries de Graaf and
               Ali Khalili and
               Benjamin Wulff and
               Steffen Lohmann and
               S{\"{o}}ren Auer},
  title     = {SlideWiki - Towards a Collaborative and Accessible Platform for Slide
               Presentations},
  booktitle = {{EC-TEL} Practitioner Proceedings 2018: 13th European Conference On
               Technology Enhanced Learning, Leeds, UK, September 3-6, 2018.},
  year      = {2018},
  crossref  = {DBLP:conf/ectel/2018p},
  url       = {http://ceur-ws.org/Vol-2193/paper6.pdf},
  timestamp = {Tue, 28 May 2019 16:23:29 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/ectel/EliasJRSGKWLA18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/esws/KhaliliBG18a,
  author    = {Ali Khalili and
               Peter van den Besselaar and
               Klaas Andries de Graaf},
  title     = {Using Linked Open Geo Boundaries for Adaptive Delineation of Functional
               Urban Areas},
  booktitle = {Proceedings of the 3rd International Workshop on Geospatial Linked
               Data and the 2nd Workshop on Querying the Web of Data co-located with
               15th Extended Semantic Web Conference {(ESWC} 2018), Heraklion, Greece,
               June 3, 2018.},
  pages     = {9--21},
  year      = {2018},
  crossref  = {DBLP:conf/esws/2018geold},
  url       = {http://ceur-ws.org/Vol-2110/paper1.pdf},
  timestamp = {Mon, 17 Jun 2019 17:36:46 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/esws/KhaliliBG18a},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/esws/KhaliliBG18b,
  author    = {Ali Khalili and
               Peter van den Besselaar and
               Klaas Andries de Graaf},
  title     = {Using Linked Open Geo Boundaries for Adaptive Delineation of Functional
               Urban Areas},
  booktitle = {The Semantic Web: {ESWC} 2018 Satellite Events - {ESWC} 2018 Satellite
               Events, Heraklion, Crete, Greece, June 3-7, 2018, Revised Selected
               Papers},
  pages     = {327--341},
  year      = {2018},
  crossref  = {DBLP:conf/esws/2018s},
  url       = {https://doi.org/10.1007/978-3-319-98192-5\_51},
  doi       = {10.1007/978-3-319-98192-5\_51},
  timestamp = {Mon, 17 Jun 2019 17:36:46 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/esws/KhaliliBG18b},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/esws/KhaliliBG18,
  author    = {Ali Khalili and
               Peter van den Besselaar and
               Klaas Andries de Graaf},
  title     = {{FERASAT:} {A} Serendipity-Fostering Faceted Browser for Linked Data},
  booktitle = {The Semantic Web - 15th International Conference, {ESWC} 2018, Heraklion,
               Crete, Greece, June 3-7, 2018, Proceedings},
  pages     = {351--366},
  year      = {2018},
  crossref  = {DBLP:conf/esws/2018},
  url       = {https://doi.org/10.1007/978-3-319-93417-4\_23},
  doi       = {10.1007/978-3-319-93417-4\_23},
  timestamp = {Mon, 17 Jun 2019 17:36:46 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/esws/KhaliliBG18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/i-semantics/KhaliliG18,
  author    = {Ali Khalili and
               Klaas Andries de Graaf},
  title     = {SlideWiki - {A} Platform for Authoring {FAIR} Educational Content},
  booktitle = {Proceedings of the Posters and Demos Track of the 14th International
               Conference on Semantic Systems co-located with the 14th International
               Conference on Semantic Systems (SEMANTiCS 2018), Vienna, Austria,
               September 10-13, 2018.},
  year      = {2018},
  crossref  = {DBLP:conf/i-semantics/2018pd},
  url       = {http://ceur-ws.org/Vol-2198/paper\_108.pdf},
  timestamp = {Tue, 28 May 2019 16:23:30 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/i-semantics/KhaliliG18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/icsa/SangwanG18,
  author    = {Raghvinder Sangwan and
               Klaas Andries de Graaf},
  title     = {{ICSA} 2018: Engineering Track: Message from the Chairs},
  booktitle = {2018 {IEEE} International Conference on Software Architecture Companion,
               {ICSA} Companion 2018, Seattle, WA, USA, April 30 - May 4, 2018},
  pages     = {76},
  year      = {2018},
  crossref  = {DBLP:conf/icsa/2018c},
  url       = {https://doi.org/10.1109/ICSA-C.2018.00028},
  doi       = {10.1109/ICSA-C.2018.00028},
  timestamp = {Mon, 05 Nov 2018 11:12:21 +0100},
  biburl    = {https://dblp.org/rec/bib/conf/icsa/SangwanG18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@phdthesis{DBLP:phd/hal/Raad18,
  author    = {Joe Raad},
  title     = {Identity Management in Knowledge Graphs. (Gestion d'identit{\'{e}}
               dans des graphes de connaissances)},
  school    = {University of Paris-Saclay, France},
  year      = {2018},
  url       = {https://tel.archives-ouvertes.fr/tel-02073961},
  timestamp = {Thu, 25 Apr 2019 11:13:30 +0200},
  biburl    = {https://dblp.org/rec/bib/phd/hal/Raad18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/ekaw/IbanescuADDGPR18,
  author    = {Liliana Ibanescu and
               Thomas Allard and
               St{\'{e}}phane Dervaux and
               Juliette Dibie and
               Elisabeth Guichard and
               Caroline P{\'{e}}nicaud and
               Joe Raad},
  title     = {A Use Case of Data Integration in Food Production},
  booktitle = {Proceedings of the {EKAW} 2018 Posters and Demonstrations Session
               co-located with 21st International Conference on Knowledge Engineering
               and Knowledge Management {(EKAW} 2018), Nancy, France, November 12-16,
               2018.},
  pages     = {17--20},
  year      = {2018},
  crossref  = {DBLP:conf/ekaw/2018pd},
  url       = {http://ceur-ws.org/Vol-2262/ekaw-poster-11.pdf},
  timestamp = {Tue, 28 May 2019 16:23:29 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/ekaw/IbanescuADDGPR18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{d589859477984c67919e8ae0d5586e4c,
  title     = "The Knowledge Graph as the Default Data Model for Machine Learning",
  abstract  = "In modern machine learning, raw data is the pre-ferred input for our models. Where a decade ago data sci-entists were still engineering features, manually picking out the details they thought salient, they now prefer the data as raw as possible. As long as we can assume that all relevant and irrelevant information is present in the input data, we can design deep models that build up intermediate represen-tations to sift out relevant features. In some areas, however, we struggle to find this raw form of data. One such area involves heterogeneous knowledge: entities, their attributes and internal relations. The Semantic Web community has in-vested decades of work on just this problem: how to repre-sent knowledge, in various domains, in as raw and as usable a form as possible, satisfying many use cases. This work has led to the Linked Open Data Cloud, a vast and distributed knowledge graph. If we can develop methods that operate on this raw form of data–the knowledge graph–we can dispense with a great deal of ad-hoc feature engineering and train deep models end-to-end in many more domains. In this position paper, we describe current research in this area and discuss some of the promises and challenges of this approach.",
  keywords  = "End-to-End Learning, Knowledge Graphs, Machine Learning, Position paper, Semantic Web",
  author    = "Xander Wilcke and Peter Bloem and {De Boer}, Victor",
  year      = "2017",
  month     = "10",
  doi       = "10.3233/DS-170007",
  pages     = "1--19",
  journal   = "Data Science",
  issn      = "2451-8484",
  publisher = "IOS Press",
}

In modern machine learning, raw data is the pre-ferred input for our models. Where a decade ago data sci-entists were still engineering features, manually picking out the details they thought salient, they now prefer the data as raw as possible. As long as we can assume that all relevant and irrelevant information is present in the input data, we can design deep models that build up intermediate represen-tations to sift out relevant features. In some areas, however, we struggle to find this raw form of data. One such area involves heterogeneous knowledge: entities, their attributes and internal relations. The Semantic Web community has in-vested decades of work on just this problem: how to repre-sent knowledge, in various domains, in as raw and as usable a form as possible, satisfying many use cases. This work has led to the Linked Open Data Cloud, a vast and distributed knowledge graph. If we can develop methods that operate on this raw form of data–the knowledge graph–we can dispense with a great deal of ad-hoc feature engineering and train deep models end-to-end in many more domains. In this position paper, we describe current research in this area and discuss some of the promises and challenges of this approach.

@article{8809d20f6f33464690787510fcb71cc7,
  title    = "An integrated approach for linked data browsing",
  abstract = "The Netherlands' Cadastre, Land Registry and Mapping Agency – in short Kadaster – collects and registers administrative and spatial data on property and the rights involved. Currently, the Kadaster is publishing its geo-spatial data assets as Linked Open Data. The Kadaster manages hundreds of datasets that describe hundreds of millions of geospatial objects, including all Dutch buildings, roads, and forests. The Kadaster exposes this large collection of data to thousands of daily users that operate from within different contexts and that need to be supported in different use cases. Therefore, Kadaster must offer diverse, yet complementary, approaches for browsing and exploring the data it publishes. Specifically, it supports the following paradigms for browsing and exploring its data assets: hierarchical browsing, graph navigation, faceted browsing, and tabular browsing. These paradigms are useful for different tasks, cover different use cases, and are implemented by reusing and/or developing Open Source libraries and applications.",
  keywords = "Data browsing, Faceted browsing, Geo-spatial data, Graph navigation, Linked open data",
  author   = "W. Beek and E. Folmer",
  note     = "FOSS4G-Europe 2017 – Academic Track, 18–22 July 2017, Marne La Vallée, France",
  year     = "2017",
  month    = "7",
  doi      = "10.5194/isprs-archives-XLII-4-W2-35-2017",
  volume   = "42",
  pages    = "35--38",
  journal  = "International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives",
  issn     = "1682-1750",
  number   = "4W2",
}

The Netherlands' Cadastre, Land Registry and Mapping Agency – in short Kadaster – collects and registers administrative and spatial data on property and the rights involved. Currently, the Kadaster is publishing its geo-spatial data assets as Linked Open Data. The Kadaster manages hundreds of datasets that describe hundreds of millions of geospatial objects, including all Dutch buildings, roads, and forests. The Kadaster exposes this large collection of data to thousands of daily users that operate from within different contexts and that need to be supported in different use cases. Therefore, Kadaster must offer diverse, yet complementary, approaches for browsing and exploring the data it publishes. Specifically, it supports the following paradigms for browsing and exploring its data assets: hierarchical browsing, graph navigation, faceted browsing, and tabular browsing. These paradigms are useful for different tasks, cover different use cases, and are implemented by reusing and/or developing Open Source libraries and applications.

@article{621c186fc6a745579df4014420e89239,
  title    = "Geoyasgui: The geosparql query editor and result set visualizer",
  abstract = "The Netherlands' Cadastre, Land Registry and Mapping Agency - in short Kadaster - collects and registers administrative and spatial data on property and the rights involved. This includes for ships, aircraft and telecommunications networks. Doing so, Kadaster protects legal certainty. The Kadaster publishes many large authoritative datasets including several key registers of the Dutch Government (Topography, Addresses and Buildings). Furthermore Kadaster is also developing and maintaining the PDOK shared service, in which about 100 spatial datasets are being published in several formats, including an incredible amount of detailed geospatial objects. Geospatial objects include all plots of land, all buildings, all roads and all lampposts. These objects are spatially and/or conceptually related, but are maintained by different data curators. As a result these datasets are syntactically and architecturally disjoint, and using them together currently requires non-trivial human labor. In response to this, Kadaster is currently publishing its geo-spatial data assets as Linked Open Data. The standardized query language for Linked Open Geodata is GeoSPARQL. Unfortunately, current tooling does not support writing and evaluating GeoSPARQL queries. This paper presents GeoYASGUI, a GeoSPARQL editor and result-set viewer with IDE capabilities. GeoYASGUI is not a new software product, but an integration of and a collection of updates to existing Open Source libraries. With GeoYASGUI it becomes possible to query the rich Open Data assets of the Kadaster.",
  keywords = "GeoSPARQL, IDE, Linked open data, Open government, Semantic web",
  author   = "W. Beek and E. Folmer and L. Rietveld and J. Walker",
  year     = "2017",
  month    = "7",
  doi      = "10.5194/isprs-archives-XLII-4-W2-39-2017",
  volume   = "42",
  pages    = "39--42",
  journal  = "International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives",
  issn     = "1682-1750",
  number   = "4W2",
}

The Netherlands' Cadastre, Land Registry and Mapping Agency - in short Kadaster - collects and registers administrative and spatial data on property and the rights involved. This includes for ships, aircraft and telecommunications networks. Doing so, Kadaster protects legal certainty. The Kadaster publishes many large authoritative datasets including several key registers of the Dutch Government (Topography, Addresses and Buildings). Furthermore Kadaster is also developing and maintaining the PDOK shared service, in which about 100 spatial datasets are being published in several formats, including an incredible amount of detailed geospatial objects. Geospatial objects include all plots of land, all buildings, all roads and all lampposts. These objects are spatially and/or conceptually related, but are maintained by different data curators. As a result these datasets are syntactically and architecturally disjoint, and using them together currently requires non-trivial human labor. In response to this, Kadaster is currently publishing its geo-spatial data assets as Linked Open Data. The standardized query language for Linked Open Geodata is GeoSPARQL. Unfortunately, current tooling does not support writing and evaluating GeoSPARQL queries. This paper presents GeoYASGUI, a GeoSPARQL editor and result-set viewer with IDE capabilities. GeoYASGUI is not a new software product, but an integration of and a collection of updates to existing Open Source libraries. With GeoYASGUI it becomes possible to query the rich Open Data assets of the Kadaster.

@inbook{c2d7a26c23234fb98ddb139ed67389f2,
  title     = "Querying Software Architecture Knowledge as Linked Open Data",
  abstract  = "It is difficult for software professionals to find all the architectural knowledge they need from architecture documentation, and this results in wasted time and mistakes in projects.This is the case even when architecture documentation is indexed by an ontology and stored in a semantic wiki.We present a prototype tool called AK-Finder which queries architectural knowledge stored in a semantic wiki as Linked Open Data using a SPARQL endpoint.Our tool retrieves knowledge from the semantic wiki and answers questions for architectural review, design, and development activities.The tool exemplifies how systems used in the software project lifecycle can integrate and improve access to architectural knowledge in a semantic wiki.",
  keywords  = "Ontology-based software architecture documentation, Architectural knowledge retrieval, Semantic wiki, Ontology, SPARQL, Query, Endpoint, Linked Open Data",
  author    = "{de Graaf}, K.A. and Anthony Tang and Peng Liang and Ali Khalili",
  year      = "2017",
  month     = "4",
  booktitle = "International Conference on Software Architecture (ICSA)",
  publisher = "IEEE",
}

It is difficult for software professionals to find all the architectural knowledge they need from architecture documentation, and this results in wasted time and mistakes in projects.This is the case even when architecture documentation is indexed by an ontology and stored in a semantic wiki.We present a prototype tool called AK-Finder which queries architectural knowledge stored in a semantic wiki as Linked Open Data using a SPARQL endpoint.Our tool retrieves knowledge from the semantic wiki and answers questions for architectural review, design, and development activities.The tool exemplifies how systems used in the software project lifecycle can integrate and improve access to architectural knowledge in a semantic wiki.

@article{c84bd6e3e40c418e85cea116fd2aacbf,
  title     = "Analyzing interactions on combining multiple clinical guidelines",
  abstract  = "Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations meant to address each condition. Although some approaches have been proposed to address this issue, important features still require more investigation, such as (re)usability and scalability. To this end, this paper presents an approach that relies on reusable rules for detecting interactions among recommendations coming from various guidelines. It extends a previously proposed knowledge representation model (TMR) to enhance the detection of interactions and it provides a systematic analysis of relevant interactions in the context of multimorbidity. The approach is evaluated in a case study on rehabilitation of breast cancer patients, developed in collaboration with experts. The results are considered promising to support the experts in this task.",
  keywords  = "Clinical knowledge representation, Combining clinical guidelines, Comorbidity, Interactions among guidelines, Multimorbidity",
  author    = "Veruska Zamborlini and {Da Silveira}, Marcos and Cedric Pruski and {ten Teije}, Annette and Edwin Geleijn and {van der Leeden}, Marike and Martijn Stuiver and {van Harmelen}, Frank",
  year      = "2017",
  month     = "3",
  doi       = "10.1016/j.artmed.2017.03.012",
  journal   = "Artificial Intelligence in Medicine",
  issn      = "0933-3657",
  publisher = "Elsevier",
}

Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations meant to address each condition. Although some approaches have been proposed to address this issue, important features still require more investigation, such as (re)usability and scalability. To this end, this paper presents an approach that relies on reusable rules for detecting interactions among recommendations coming from various guidelines. It extends a previously proposed knowledge representation model (TMR) to enhance the detection of interactions and it provides a systematic analysis of relevant interactions in the context of multimorbidity. The approach is evaluated in a case study on rehabilitation of breast cancer patients, developed in collaboration with experts. The results are considered promising to support the experts in this task.

@article{f79133cc037d4f42a6bd4bd84d446488,
  title     = "Meta-data for a lot of LOD",
  abstract  = "This paper introduces the LOD Laundromat meta-dataset, a continuously updated RDF meta-dataset that describes the documents crawled, cleaned and (re)published by the LOD Laundromat. This meta-dataset of over 110 million triples contains structural information for more than 650,000 documents (and growing). Dataset meta-data is often not provided alongside published data, it is incomplete or it is incomparable given the way they were generated. The LOD Laundromat meta-dataset provides a wide range of structural dataset properties, such as the number of triples in LOD Laundromat documents, the average degree in documents, and the distinct number of Blank Nodes, Literals and IRIs. This makes it a particularly useful dataset for data comparison and analytics, as well as for the global study of the Web of Data. This paper presents the dataset, its requirements, and its impact.",
  author    = "Laurens Rietveld and Wouter Beek and Rinke Hoekstra and Stefan Schlobach",
  year      = "2017",
  month     = "1",
  doi       = "10.3233/SW-170256",
  volume    = "8",
  pages     = "1067--1080",
  journal   = "Semantic Web",
  issn      = "1570-0844",
  publisher = "IOS Press",
  number    = "6",
}

This paper introduces the LOD Laundromat meta-dataset, a continuously updated RDF meta-dataset that describes the documents crawled, cleaned and (re)published by the LOD Laundromat. This meta-dataset of over 110 million triples contains structural information for more than 650,000 documents (and growing). Dataset meta-data is often not provided alongside published data, it is incomplete or it is incomparable given the way they were generated. The LOD Laundromat meta-dataset provides a wide range of structural dataset properties, such as the number of triples in LOD Laundromat documents, the average degree in documents, and the distinct number of Blank Nodes, Literals and IRIs. This makes it a particularly useful dataset for data comparison and analytics, as well as for the global study of the Web of Data. This paper presents the dataset, its requirements, and its impact.

@misc{163c47c85bee47e38861a09ed5a1253c,
  title  = "A linked open data based system for flexible delineation of geographic areas",
  author = "A. Khalili and {van den Besselaar}, P.A.A. and {de Graaf}, K.A.",
  year   = "2017",
}

@inbook{4c7885c87ca2424b84f30e0ce5fbf6b0,
  title     = "An empirical study on how the distribution of ontologies affects reasoning on the web",
  abstract  = "The Web of Data is an inherently distributed environment where ontologies are located in (physically) remote locations and are subject to constant changes. Reasoning is affected by these changes, but the extent and significance of this dependency is not well-studied yet. To address this problem, this paper presents an empirical study on how the distribution of ontological data on the Web affects the outcome of reasoning. We study (1) to what degree datasets depend on external ontologies and (2) to what extent the inclusion of additional ontological information via IRI de-referencing and the owl:imports directive to the input datasets leads to new derivations. We based our study on many RDF datasets and on a large collection of RDFa, and JSON-LD data embedded into HTML pages. We used both Jena and Pellet in order to evaluate the results under different semantics. Our results indicate that remote ontologies are often crucial to obtain non-trivial derivations. Unfortunately, in many cases IRIs were broken and the owl:imports is rarely used. Furthermore, in some cases the inclusion of remote knowledge either did not yield any additional derivation or led to errors. Despite these cases, in general, we found that inclusion of additional ontologies via IRIs de-referencing and owl:imports directive is very effective for producing new derivations. This indicates that the two W3C standards for fetching remote ontologies have found their way into practice.",
  keywords  = "JSON-LD, OWL, RDF, RDFa, Reasoning, Web of data",
  author    = "Bazoobandi, {Hamid R.} and Jacopo Urbani and {van Harmelen}, Frank and Henri Bal",
  year      = "2017",
  doi       = "10.1007/978-3-319-68288-4_5",
  isbn      = "9783319682877",
  volume    = "10587 LNCS",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "69--86",
  booktitle = "The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings",
}

The Web of Data is an inherently distributed environment where ontologies are located in (physically) remote locations and are subject to constant changes. Reasoning is affected by these changes, but the extent and significance of this dependency is not well-studied yet. To address this problem, this paper presents an empirical study on how the distribution of ontological data on the Web affects the outcome of reasoning. We study (1) to what degree datasets depend on external ontologies and (2) to what extent the inclusion of additional ontological information via IRI de-referencing and the owl:imports directive to the input datasets leads to new derivations. We based our study on many RDF datasets and on a large collection of RDFa, and JSON-LD data embedded into HTML pages. We used both Jena and Pellet in order to evaluate the results under different semantics. Our results indicate that remote ontologies are often crucial to obtain non-trivial derivations. Unfortunately, in many cases IRIs were broken and the owl:imports is rarely used. Furthermore, in some cases the inclusion of remote knowledge either did not yield any additional derivation or led to errors. Despite these cases, in general, we found that inclusion of additional ontologies via IRIs de-referencing and owl:imports directive is very effective for producing new derivations. This indicates that the two W3C standards for fetching remote ontologies have found their way into practice.

@book{fcb11c81af894034a33d5a6d705166b0,
  title     = "ARIADNE: Final Report on Data Mining",
  abstract  = "Recent years have witnessed a growing interest from archaeological communities in Linked Data. ARIADNE, the AdvancedResearch Infrastructure for Archaeological Data set Networking in Europe, facilitates a central web portal that providesaccess to archaeological data from various sources. Parts of these data have been being published as Linked Data, andare currently available in the Linked Open Data cloud. With it, the nature of these data has shifted from unstructuredto structured. This presents new opportunities for data mining. While general-purpose software exists, recent studieshave revealed the importance of two domain-specific requirements: 1) produce interpretable results, and 2) allow trustin the underlying model. In this work, we investigate to what extend interpretable data mining can contribute to theunderstanding of linked archaeological data. A case study washeld, which involved the mining of semantic association rules over data sets of increasing levels of knowledgegranularity, followed by the qualitative evaluation of these rules by domain experts. Experiments have shown that theapproach yielded mostly plausible patterns, some of which were seen as highly relevant.",
  author    = "W.X. Wilcke and {de Boer}, V. and {van Harmelen}, F.A.H. and {de Kleijn}, M.T.M. and M. Wansleeben and Harry Dimitropoulos and Holly Wright",
  year      = "2017",
  series    = "ARIADNE",
  publisher = "Ariadne",
  number    = "D16.3",
}

Recent years have witnessed a growing interest from archaeological communities in Linked Data. ARIADNE, the AdvancedResearch Infrastructure for Archaeological Data set Networking in Europe, facilitates a central web portal that providesaccess to archaeological data from various sources. Parts of these data have been being published as Linked Data, andare currently available in the Linked Open Data cloud. With it, the nature of these data has shifted from unstructuredto structured. This presents new opportunities for data mining. While general-purpose software exists, recent studieshave revealed the importance of two domain-specific requirements: 1) produce interpretable results, and 2) allow trustin the underlying model. In this work, we investigate to what extend interpretable data mining can contribute to theunderstanding of linked archaeological data. A case study washeld, which involved the mining of semantic association rules over data sets of increasing levels of knowledgegranularity, followed by the qualitative evaluation of these rules by domain experts. Experiments have shown that theapproach yielded mostly plausible patterns, some of which were seen as highly relevant.

@article{ba103be04d354166b43301342eb3215b,
  title     = "CEDAR: The Dutch Historical Censuses as Linked Open Data",
  abstract  = "Here, we describe the CEDAR dataset, a five-star Linked Open Data representation of the Dutch historical censuses. These were conducted in the Netherlands once every 10 years from 1795 to 1971. We produce a linked dataset from a digitized sample of 2,288 tables. It contains more than 6.8 million statistical observations about the demography, labour and housing of Dutch society in the 18th, 19th and 20th centuries. The dataset is modeled using the RDF Data Cube, Open Annotation, and PROV vocabularies. These are used to represent the multidimensionality of the data, to express rules of data harmonization, and to keep track of the provenance of all data points and their transformations, respectively. We link observations within the dataset to well known standard classification systems in social history, such as the Historical International Standard Classification of Occupations (HISCO) and the Amsterdamse Code (AC). The three contributions of the dataset are (1) an easier access to integrated census data for historical researchers; (2) richer connections to related Linked Data resources; and (3) novel concept schemes of historical relevance, like classifications of historical religions and historical house types.",
  keywords  = "census data, Linked Open Data, RDF Data Cube, Social history",
  author    = "Albert Meroño-Peñuela and Ashkan Ashkpour and Christophe Guéret and Stefan Schlobach",
  year      = "2017",
  doi       = "10.3233/SW-160233",
  volume    = "8",
  pages     = "297--310",
  journal   = "Semantic Web",
  issn      = "1570-0844",
  publisher = "IOS Press",
  number    = "2",
}

Here, we describe the CEDAR dataset, a five-star Linked Open Data representation of the Dutch historical censuses. These were conducted in the Netherlands once every 10 years from 1795 to 1971. We produce a linked dataset from a digitized sample of 2,288 tables. It contains more than 6.8 million statistical observations about the demography, labour and housing of Dutch society in the 18th, 19th and 20th centuries. The dataset is modeled using the RDF Data Cube, Open Annotation, and PROV vocabularies. These are used to represent the multidimensionality of the data, to express rules of data harmonization, and to keep track of the provenance of all data points and their transformations, respectively. We link observations within the dataset to well known standard classification systems in social history, such as the Historical International Standard Classification of Occupations (HISCO) and the Amsterdamse Code (AC). The three contributions of the dataset are (1) an easier access to integrated census data for historical researchers; (2) richer connections to related Linked Data resources; and (3) novel concept schemes of historical relevance, like classifications of historical religions and historical house types.

@inbook{db25399349794a8f86dce8ed0ec8ac69,
  title     = "Constructing disease-centric knowledge graphs: A case study for depression (short version)",
  abstract  = "In this paper we show how we used multiple large knowledge sources to construct a much smaller knowledge graph that is focussed on single disease (in our case major depression disorder). Such a disease-centric knowledge-graph makes it more convenient for doctors (in our case psychiatric doctors) to explore the relationship among various knowledge resources and to answer realistic clinical queries.",
  author    = "Zhisheng Huang and Jie Yang and {van Harmelen}, Frank and Qing Hu",
  year      = "2017",
  doi       = "10.1007/978-3-319-59758-4_5",
  isbn      = "9783319597577",
  volume    = "10259 LNAI",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "48--52",
  booktitle = "Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings",
}

In this paper we show how we used multiple large knowledge sources to construct a much smaller knowledge graph that is focussed on single disease (in our case major depression disorder). Such a disease-centric knowledge-graph makes it more convenient for doctors (in our case psychiatric doctors) to explore the relationship among various knowledge resources and to answer realistic clinical queries.

@inbook{0a3a2ec2685448339a2ca93455e0e36f,
  title     = "Constructing Knowledge Graphs of Depression",
  abstract  = "Knowledge Graphs have been shown to be useful tools for integrating multiple medical knowledge sources, and to support such tasks as medical decision making, literature retrieval, determining healthcare quality indicators, co-morbodity analysis and many others. A large number of medical knowledge sources have by now been converted to knowledge graphs, covering everything from drugs to trials and from vocabularies to gene-disease associations. Such knowledge graphs have typically been generic, covering very large areas of medicine. (e.g. all of internal medicine, or arbitrary drugs, arbitrary trials, etc.). This has had the effect that such knowledge graphs become prohibitively large, hampering both efficiency for machines and usability for people. In this paper we show how we use multiple large knowledge sources to construct a much smaller knowledge graph that is focussed on single disease (in our case major depression disorder). Such a disease-centric knowledge-graph makes it more convenient for doctors (in our case psychiatric doctors) to explore the relationship among various knowledge resources and to answer realistic clinical queries (This paper is an extended version of [1].).",
  author    = "Zhisheng Huang and Jie Yang and {van Harmelen}, Frank and Qing Hu",
  year      = "2017",
  doi       = "10.1007/978-3-319-69182-4_16",
  isbn      = "9783319691817",
  volume    = "10594 LNCS",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "149--161",
  booktitle = "Health Information Science - 6th International Conference, HIS 2017, Proceedings",
}

Knowledge Graphs have been shown to be useful tools for integrating multiple medical knowledge sources, and to support such tasks as medical decision making, literature retrieval, determining healthcare quality indicators, co-morbodity analysis and many others. A large number of medical knowledge sources have by now been converted to knowledge graphs, covering everything from drugs to trials and from vocabularies to gene-disease associations. Such knowledge graphs have typically been generic, covering very large areas of medicine. (e.g. all of internal medicine, or arbitrary drugs, arbitrary trials, etc.). This has had the effect that such knowledge graphs become prohibitively large, hampering both efficiency for machines and usability for people. In this paper we show how we use multiple large knowledge sources to construct a much smaller knowledge graph that is focussed on single disease (in our case major depression disorder). Such a disease-centric knowledge-graph makes it more convenient for doctors (in our case psychiatric doctors) to explore the relationship among various knowledge resources and to answer realistic clinical queries (This paper is an extended version of [1].).

@inbook{f0a401b2137b45e5b2d32d0149c3160a,
  title     = "Detecting New Evidences for Evidence-Based Medical Guidelines with Journal Filtering",
  abstract  = "Evidence-based medical guidelines are systematically developed recommendations with the aim to assist practitioner and patients decisions regarding appropriate health care for specific clinical circumstances, and are based on evidence described in medical research papers. Evidence-based medical guidelines should be regularly updated, such that they can serve medical practice using based on the latest medical research evidence. A usual approach to detecting new evidences is to use a set of terms which appear in a guideline conclusion or recommendation and create queries over a bio-medical search engine such as PubMed with a ranking over a selected subset of terms to search for relevant new research papers. However, the sizes of the found relevant papers are usually very large (i.e. over a few hundreds, even thousands), which results in a low precision of the search. This makes it for medical professionals quite difficult to find which papers are really interesting and useful for updating the guideline. We propose a filtering step to decrease the number of papers. More exactly we are interested in the question if we can reduce the number of papers with no or a slightly lower recall. A plausible approach is to introduce journal filtering, such that evidence appear in those top journals are preferred. In this paper, we extend our approach of detecting new papers for updating evidence-based medical guideline with a journal filtering step. We report our experiments and show that (1) the method with journal filtering can indeed gain a large reduction of the number of papers (69.73%) with a slightly lower recall (14.29%); (2) we show that the journal filtering method keeps relatively more high level evidence papers (category A) and removes all the low level evidence papers (category D).",
  author    = "Qing Hu and Zisheng Huang and {ten Teije}, Annette and {van Harmelen}, Frank",
  year      = "2017",
  doi       = "10.1007/978-3-319-55014-5_8",
  isbn      = "9783319550138",
  volume    = "10096 LNAI",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "120--132",
  editor    = "David Riaño and Richard Lenz and Manfred Reichert",
  booktitle = "Knowledge Representation for Health Care: HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Munich, Germany, September 2, 2016, Revised Selected Papers",
}

Evidence-based medical guidelines are systematically developed recommendations with the aim to assist practitioner and patients decisions regarding appropriate health care for specific clinical circumstances, and are based on evidence described in medical research papers. Evidence-based medical guidelines should be regularly updated, such that they can serve medical practice using based on the latest medical research evidence. A usual approach to detecting new evidences is to use a set of terms which appear in a guideline conclusion or recommendation and create queries over a bio-medical search engine such as PubMed with a ranking over a selected subset of terms to search for relevant new research papers. However, the sizes of the found relevant papers are usually very large (i.e. over a few hundreds, even thousands), which results in a low precision of the search. This makes it for medical professionals quite difficult to find which papers are really interesting and useful for updating the guideline. We propose a filtering step to decrease the number of papers. More exactly we are interested in the question if we can reduce the number of papers with no or a slightly lower recall. A plausible approach is to introduce journal filtering, such that evidence appear in those top journals are preferred. In this paper, we extend our approach of detecting new papers for updating evidence-based medical guideline with a journal filtering step. We report our experiments and show that (1) the method with journal filtering can indeed gain a large reduction of the number of papers (69.73%) with a slightly lower recall (14.29%); (2) we show that the journal filtering method keeps relatively more high level evidence papers (category A) and removes all the low level evidence papers (category D).

@misc{1db5e48c46a24fdfbe5f7395eeeaccec,
  title  = "Enriching Scientometrics with Linked Data: the SMS platform",
  author = "O.A.K. Idrissou and A. Khalili and {van den Besselaar}, P.A.A. and {de Graaf}, K.A. and A. Loizou and {van Harmelen}, Frank",
  year   = "2017",
}

@inbook{e64aef34c4ba4b16841be18a17276f21,
  title     = "Evaluating research portfolios, a method and a case",
  author    = "{van den Besselaar}, P.A.A. and A. Khalili and Ulf Sandstrom",
  year      = "2017",
  booktitle = "Proceedings STI 2017 conference",
}

@misc{a6ba9f04e8e24b2c955199260697e8d0,
  title  = "Evaluating research portfolio's through ontology based text annotation",
  author = "{van den Besselaar}, P.A.A. and A. Khalili and Ulf Sandstrom",
  year   = "2017",
}

@inbook{cb46e483292d4cd99b533f825c47870e,
  title     = "Formalization and Computation of Diabetes Quality Indicators with Patient Data from a Chinese Hospital",
  abstract  = "Clinical quality indicators are tools to measure the quality of healthcare and can be classified into structure-related, process-related and outcome-related indicators. The objective of this study is to investigate whether Electronic Medical Record (EMR) data from a Chinese diabetes specialty hospital can be used for the automated computation of a set of 38 diabetes quality indicators, especially process-related indicators. The clinical quality indicator formalization (CLIF) method and tool and SNOMED CT were adopted to formalize diabetes indicators into executable queries. The formalized indicators were run on the patient data to test the feasibility of their automated computation. In this study, we successfully formalized and computed 32 of 38 quality indicators based on the EMR data. The results indicate that the data from our Chinese EMR can be used for the formalization and computation of most diabetes indicators, but that it can be improved to support the computation of more indicators.",
  keywords  = "Clinical quality, Diabetes mellitus, Electronic medical record, Formalization, Quality indicators, Secondary use of patient data",
  author    = "Haitong Liu and {ten Teije}, Annette and Kathrin Dentler and Jingdong Ma and Shijing Zhang",
  year      = "2017",
  doi       = "10.1007/978-3-319-55014-5_2",
  isbn      = "9783319550138",
  volume    = "10096 LNAI",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "23--35",
  editor    = "David Riaño and Richard Lenz and Manfred Reichert",
  booktitle = "Knowledge Representation for Health Care: HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Munich, Germany, September 2, 2016, Revised Selected Papers",
}

Clinical quality indicators are tools to measure the quality of healthcare and can be classified into structure-related, process-related and outcome-related indicators. The objective of this study is to investigate whether Electronic Medical Record (EMR) data from a Chinese diabetes specialty hospital can be used for the automated computation of a set of 38 diabetes quality indicators, especially process-related indicators. The clinical quality indicator formalization (CLIF) method and tool and SNOMED CT were adopted to formalize diabetes indicators into executable queries. The formalized indicators were run on the patient data to test the feasibility of their automated computation. In this study, we successfully formalized and computed 32 of 38 quality indicators based on the EMR data. The results indicate that the data from our Chinese EMR can be used for the formalization and computation of most diabetes indicators, but that it can be improved to support the computation of more indicators.

@inbook{ea956dc374914693bcdda59a8557174d,
  title     = "Fostering Serendipitous Knowledge Discovery using an Adaptive Multigraph-based Faceted Browser",
  abstract  = "Serendipity, the art of making an unsought finding plays also an important role in the emerging field of data science, allowing the discovery of interesting and valuable facts not initially sought for. Previous research has extracted many serendipity-fostering patterns applicable to digital data-driven systems. Linked Open Data (LOD) on the Web which is powered by the Follow-Your-Nose effect, provides already a rich source for serendipity. The serendipity most often takes place when browsing data. Therefore, flexible and intuitive browsing user interfaces which support serendipity triggers such as enigmas, anomalies and novelties, can increase the likelihood of serendipity on LOD. In this work, we propose a set of serendipity-fostering design features supported by an adaptive multigraph-based faceted browsing interface to catalyze serendipity on Semantic Web and LOD environments.",
  author    = "Ali Khalili and {van Andel}, Pek and {van den Besselaar}, Peter and {de Graaf}, {Klaas Andries}",
  year      = "2017",
  doi       = "10.1145/3148011.3148037",
  isbn      = "978-1-4503-5553-7/17/12",
  booktitle = "The Ninth International Conference on Knowledge Capture: K-CAP 2017",
  publisher = "ACM",
}

Serendipity, the art of making an unsought finding plays also an important role in the emerging field of data science, allowing the discovery of interesting and valuable facts not initially sought for. Previous research has extracted many serendipity-fostering patterns applicable to digital data-driven systems. Linked Open Data (LOD) on the Web which is powered by the Follow-Your-Nose effect, provides already a rich source for serendipity. The serendipity most often takes place when browsing data. Therefore, flexible and intuitive browsing user interfaces which support serendipity triggers such as enigmas, anomalies and novelties, can increase the likelihood of serendipity on LOD. In this work, we propose a set of serendipity-fostering design features supported by an adaptive multigraph-based faceted browsing interface to catalyze serendipity on Semantic Web and LOD environments.

@misc{68a3538d56a949669e47b156b3b5b212,
  title  = "From university rankings to portfolio analysis and evaluation",
  author = "{van den Besselaar}, P.A.A. and A. Khalili and Ulf Sandstrom",
  year   = "2017",
}

@inbook{68afba2d8f25485fa3ade85bf8a66553,
  title     = "Generalizing the Detection of Clinical Guideline Interactions Enhanced with LOD",
  author    = "{Carretta Zamborlini}, Veruska and Rinke Hoekstra and {Da Silveira}, Marcos and Cedric Pruski and {ten Teije}, Annette and {van Harmelen}, Frank",
  year      = "2017",
  doi       = "10.1007/978-3-319-54717-6_20",
  isbn      = "978-3-319-54717-6",
  pages     = "360--386",
  editor    = "Ana Fred and Hugo Gamboa",
  booktitle = "Biomedical Engineering Systems and Technologies: 9th International Joint Conference, BIOSTEC 2016, Rome, Italy, February 21--23, 2016, Revised Selected Papers",
  publisher = "Springer International Publishing Switzerland",
}

@misc{a48b60672af940c4a911566a973f08aa,
  title  = "Improving social research using heterogeneous data: the SMS platform",
  author = "{van den Besselaar}, P.A.A. and A. Khalili and {van Harmelen}, Frank and {de Graaf}, K.A.",
  year   = "2017",
}

@inbook{336aadbd02e346dcadb4630b4fe216fa,
  title     = "Is my:sameAs the same as your:sameAs?",
  abstract  = "Linking between entities in different datasets is a crucial element of the Semantic Web architecture, since those links allow us to integrate datasets without having to agree on a uniform vocabulary. However, it is widely acknowledged that the owl:sameAs construct is too blunt a tool for this purpose. It entails full equality between two resources independent of context. But whether or not two resources should be considered equal depends not only on their intrinsic properties, but also on the purpose or task for which the resources are used. We present a system for constructing contextspecific equality links. In a first step, our system generates a set of probable links between two given datasets. These potential links are decorated with rich metadata describing how, why, when and by whom they were generated. In a second step, a user then selects the links which are suited for the current task and context, constructing a context-specific “Lenticular Lens”. Such lenses can be combined using operators such as union, intersection, difference and composition. We illustrate and validate our approach with",
  author    = "Idrissou, {Al Koudous} and Rinke Hoekstra and {van Harmelen}, Frank and Ali Khalili and {van den Besselaar}, Peter",
  year      = "2017",
  booktitle = "The ninth international conference on knowledge capture: k-cap 2017",
}

Linking between entities in different datasets is a crucial element of the Semantic Web architecture, since those links allow us to integrate datasets without having to agree on a uniform vocabulary. However, it is widely acknowledged that the owl:sameAs construct is too blunt a tool for this purpose. It entails full equality between two resources independent of context. But whether or not two resources should be considered equal depends not only on their intrinsic properties, but also on the purpose or task for which the resources are used. We present a system for constructing contextspecific equality links. In a first step, our system generates a set of probable links between two given datasets. These potential links are decorated with rich metadata describing how, why, when and by whom they were generated. In a second step, a user then selects the links which are suited for the current task and context, constructing a context-specific “Lenticular Lens”. Such lenses can be combined using operators such as union, intersection, difference and composition. We illustrate and validate our approach with

@inbook{5d2c3965da59435382ceb720c06021ad,
  title     = "Knowledge-Driven Paper Retrieval to Support Updating of Clinical Guidelines",
  author    = "{Carretta Zamborlini}, Veruska and Qing Hu and Z. Huang and {Da Silveira}, Marcos and Cedric Pruski and {ten Teije}, A.C.M. and {van Harmelen}, F.A.H.",
  year      = "2017",
  doi       = "10.1007/978-3-319-55014-5_5",
  isbn      = "978-3-319-55014-5",
  pages     = "71--89",
  editor    = "David Riaño and Richard Lenz and Manfred Reichert",
  booktitle = "Knowledge Representation for Health Care: HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Munich, Germany, September 2, 2016, Revised Selected Papers",
  publisher = "Springer International Publishing Switzerland",
}

@misc{5f262cbfd4654e7da5153fc5a03cdc6a,
  title    = "Knowledge Representation for Clinical Guidelines: with applications to Multimorbidity Analysis and Literature Search",
  keywords = "Knowledge Representation, Semantic Web, Clinical Guidelines, Multimorbidity, Comorbidity, Guideline Update",
  author   = "{Carretta Zamborlini}, V.",
  year     = "2017",
  school   = "Vrije Universiteit Amsterdam",
}

@inbook{d84639693b2349e3b8afcf9dacfd35d0,
  title     = "Linked Data Reactor: Towards Data-aware User Interfaces",
  author    = "Ali Khalili and {de Graaf}, {Klaas Andries}",
  year      = "2017",
  booktitle = "SEMANTiCS 2017",
}

@inbook{488e681637ff47f4bf544f43c457ec82,
  title     = "LOD-a-lot: A queryable dump of the LOD cloud",
  abstract  = "LOD-a-lot democratizes access to the Linked Open Data (LOD) Cloud by serving more than 28 billion unique triples from 650, K datasets over a single self-indexed file. This corpus can be queried online with a sustainable Linked Data Fragments interface, or downloaded and consumed locally: LOD-a-lot is easy to deploy and demands affordable resources (524, GB of disk space and 15.7, GB of RAM), enabling Web-scale repeatable experimentation and research even by standard laptops.",
  author    = "Fernández, {Javier D.} and Wouter Beek and Martínez-Prieto, {Miguel A.} and Mario Arias",
  year      = "2017",
  doi       = "10.1007/978-3-319-68204-4_7",
  isbn      = "9783319682037",
  volume    = "10588 LNCS",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "75--83",
  booktitle = "The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings",
}

LOD-a-lot democratizes access to the Linked Open Data (LOD) Cloud by serving more than 28 billion unique triples from 650, K datasets over a single self-indexed file. This corpus can be queried online with a sustainable Linked Data Fragments interface, or downloaded and consumed locally: LOD-a-lot is easy to deploy and demands affordable resources (524, GB of disk space and 15.7, GB of RAM), enabling Web-scale repeatable experimentation and research even by standard laptops.

@inbook{97e7492b3f604d8597c2ea8af941a33e,
  title     = "LOD lab: Scalable linked data processing",
  abstract  = "With tens if not hundreds of billions of logical statements, the Linked Open Data (LOD) is one of the biggest knowledge bases ever built. As such it is a gigantic source of information for applications in various domains, but also given its size an ideal test-bed for knowledge representation and reasoning, heterogeneous nature, and complexity. However, making use of this unique resource has proven next to impossible in the past due to a number of problems, including data collection, quality, accessibility, scalability, availability and findability. The LOD Laundromat and LOD Lab are recent infrastructures that addresses these problems in a systematic way, by automatically crawling, cleaning, indexing, analysing and republishing data in a unified way. Given a family of simple tools, LOD Lab allows researchers to query, access, analyse and manipulate hundreds of thousands of data documents seamlessly, e.g. facilitating experiments (e.g. for reasoning) over hundreds of thousands of (possibly integrated) datasets based on content and meta-data. This chapter provides the theoretical basis and practical skills required for making ideal use of this large scale experimental platform. First we study the problems that make it so hard to work with Semantic Web data in its current form. We’ll also propose generic solutions and introduce the tools the reader needs to get started with their own experiments on the LOD Cloud.",
  author    = "Wouter Beek and Laurens Rietveld and F. Ilievski and Stefan Schlobach",
  year      = "2017",
  doi       = "10.1007/978-3-319-49493-7_4",
  isbn      = "9783319494920",
  volume    = "9885 LNCS",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "124--155",
  booktitle = "Reasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering - 12th International Summer School 2016, Tutorial Lectures",
}

With tens if not hundreds of billions of logical statements, the Linked Open Data (LOD) is one of the biggest knowledge bases ever built. As such it is a gigantic source of information for applications in various domains, but also given its size an ideal test-bed for knowledge representation and reasoning, heterogeneous nature, and complexity. However, making use of this unique resource has proven next to impossible in the past due to a number of problems, including data collection, quality, accessibility, scalability, availability and findability. The LOD Laundromat and LOD Lab are recent infrastructures that addresses these problems in a systematic way, by automatically crawling, cleaning, indexing, analysing and republishing data in a unified way. Given a family of simple tools, LOD Lab allows researchers to query, access, analyse and manipulate hundreds of thousands of data documents seamlessly, e.g. facilitating experiments (e.g. for reasoning) over hundreds of thousands of (possibly integrated) datasets based on content and meta-data. This chapter provides the theoretical basis and practical skills required for making ideal use of this large scale experimental platform. First we study the problems that make it so hard to work with Semantic Web data in its current form. We’ll also propose generic solutions and introduce the tools the reader needs to get started with their own experiments on the LOD Cloud.

@misc{c38c16616d624e5799d49a5241a73f30,
  title    = "Logics for causal inference under uncertainty",
  keywords = "causality, causal discovery, probabilistic logics, structure learning, probabilistic soft logic",
  author   = "S. Magliacane",
  year     = "2017",
  school   = "Vrije Universiteit Amsterdam",
}

@book{1539ee1ae92840a1beb2609e405321d0,
  title     = "Opening the SMS platform to users: Deliverable D7.2 - RISIS project",
  abstract  = "In this deliverable we describe the SMS (Semantically Mapping Science) data integration platform (http://sms.risis.eu), the technical core within the RISIS data infrastructure for Science, Technology and Innovation Studies (STI). The aim of the platform is to produce richer data to be used in social research – through the integration of heterogeneous datasets, ranging from tabular statistical data to unstructured data found on the Web. We outline the platform’s architecture and functions. There arealso some example use cases mentioned to show how the platform enables data integration in practice.",
  author    = "{van den Besselaar}, P.A.A. and A. Khalili and {de Graaf}, K.A. and O.A.K. Idrissou and {van Harmelen}, Frank",
  year      = "2017",
  publisher = "RISIS",
}

In this deliverable we describe the SMS (Semantically Mapping Science) data integration platform (http://sms.risis.eu), the technical core within the RISIS data infrastructure for Science, Technology and Innovation Studies (STI). The aim of the platform is to produce richer data to be used in social research – through the integration of heterogeneous datasets, ranging from tabular statistical data to unstructured data found on the Web. We outline the platform’s architecture and functions. There arealso some example use cases mentioned to show how the platform enables data integration in practice.

@book{beaec0ed935045dba04a24fd27207fae,
  title     = "Report on the Finalization of Geocoding and Geo-clustering Activities and Preliminary Analysis of European Innovation Dynamics: RISIS deliverable D9.3",
  author    = "Eran Leck and Daphne Getz and A. Khalili and {van den Besselaar}, P.A.A. and et. al.",
  year      = "2017",
  publisher = "RISIS",
}

@inbook{1ea4f94b25b648a69d0416b2d937605c,
  title     = "Semantically Mapping Science (SMS) Platform",
  author    = "Ali Khalili and {van den Besselaar}, Peter and {Al Koudous}, Idrissou and {de Graaf}, {Klaas Andries} and {van Harmelen}, Frank",
  year      = "2017",
  pages     = "1--6",
  booktitle = "SemSci 2017: Enabling Open Semantic Science",
  publisher = "CEUR Workshop Proceedings",
}

@misc{61aa29f69da34a6f832f7f20ba0ac5ea,
  title  = "SMS: a platform for linking and enriching data for science and innovation studies: An example",
  author = "{van den Besselaar}, P.A.A. and A. Khalili and O.A.K. Idrissou and {de Graaf}, K.A. and {van Harmelen}, Frank",
  year   = "2017",
}

@article{60914ca9ba2543df8bf408a89914faa8,
  title     = "Understanding knowledge networks",
  abstract  = "The emergence of Linked Open Data (LOD) enables data on the Web to have a well defined structure and thereby to represent and in? terlink information from different sources and application areas. This web of data is a complex socially created network, where concepts and rela? tions are connected in intricate ways, collectively forming a network of knowledge. These data are published in a decentralized fashion and they stem from different sources, have different types of relationships, and use different terminologies, ontologies and meta models. While this so-called LOD cloud has become a very valuable resource, we know only very little about the general structural properties of the contained data, which im? pedes our ability to use and organize this resource in an efficient and accu? rate manner. The objective of this paper is to provide a basic understand? ing of LOD from the point of view of network structures. We analyze LOD networks with respect to fundamental network properties such as degree distribution and clustering. Using these metrics, we compare our results to non-LOD networks, such as email, Web, and protein networks, that have been reported in the literature. Our results show that the LOD cloud ex? hibits a broad variety of different network structures, consistent with the diversity found in other types of networks.",
  keywords  = "Knowledge network, Linked data, Network structure",
  author    = "Krishna Mangaladevi and Wouter Beek and Tobias Kuhn",
  year      = "2017",
  volume    = "1946",
  pages     = "38--49",
  journal   = "CEUR workshop proceedings",
  issn      = "1613-0073",
  publisher = "CEUR Workshop Proceedings",
}

The emergence of Linked Open Data (LOD) enables data on the Web to have a well defined structure and thereby to represent and in? terlink information from different sources and application areas. This web of data is a complex socially created network, where concepts and rela? tions are connected in intricate ways, collectively forming a network of knowledge. These data are published in a decentralized fashion and they stem from different sources, have different types of relationships, and use different terminologies, ontologies and meta models. While this so-called LOD cloud has become a very valuable resource, we know only very little about the general structural properties of the contained data, which im? pedes our ability to use and organize this resource in an efficient and accu? rate manner. The objective of this paper is to provide a basic understand? ing of LOD from the point of view of network structures. We analyze LOD networks with respect to fundamental network properties such as degree distribution and clustering. Using these metrics, we compare our results to non-LOD networks, such as email, Web, and protein networks, that have been reported in the literature. Our results show that the LOD cloud ex? hibits a broad variety of different network structures, consistent with the diversity found in other types of networks.

@inbook{8c1aa17784b541469b5e2520b80a7746,
  title     = "Visualizing Linked Data as Habitable Cities",
  author    = "{de Graaf}, {Klaas Andries} and Ali Khalili",
  year      = "2017",
  booktitle = "3rd Workshop on Visualization and Interaction for Ontologies and Linked Data (VOILA)",
}

@inbook{18f7f3aa55944236966cf716a46f80d6,
  title     = "WYSIWYQ -- What You See Is What You Query",
  author    = "Ali Khalili and Albert Meroño-Peñuela",
  year      = "2017",
  booktitle = "Visualization and Interaction for Ontologies and Linked Data (VOILA) 2017",
}

@article{16800c26e53243ca86fbe34eaf7de0c8,
  title   = "基于知识图谱的精细化工辅助研发平台",
  author  = "Zhisheng Huang and Jinguang Gu and Bin Peng",
  year    = "2017",
  volume  = "3",
  pages   = "43--55",
  journal = "Journal of Technology Intelligence Engineering",
  issn    = "2095-915X",
  number  = "1",
}

@article{82074b235a8a424bb7645c2c2887f457,
  title     = "The YASGUI family of SPARQL clients",
  abstract  = "The size and complexity of the Semantic Web and its technology stack makes it difficult to query. Access to Linked Data could be greatly facilitated if it were supported by a tool with a strong focus on usability. In this paper we present the YASGUI family of SPARQL clients, a continuation of the YASGUI tool introduced more than two years ago. The YASGUI family of SPARQL clients enables publishers to improve ease of access for their SPARQL endpoints, and gives consumers of Linked Data a robust, feature-rich and user friendly SPARQL editor. We show that the YASGUI family had significant impact on the landscape of Linked Data management: YASGUI components are integrated in state-of-the-art triple-stores and Linked Data applications, and used as front-end by a large number of Linked Data publishers. Additionally, we show that the YASGUI web service - which provides access to any SPARQL endpoint - has a large and growing user base amongst Linked Data consumers.",
  keywords  = "data publishing, Linked Data, query formulation, SPARQL",
  author    = "Laurens Rietveld and Rinke Hoekstra",
  year      = "2016",
  month     = "12",
  doi       = "10.3233/SW-150197",
  volume    = "8",
  pages     = "373--383",
  journal   = "Semantic Web",
  issn      = "1570-0844",
  publisher = "IOS Press",
  number    = "3",
}

The size and complexity of the Semantic Web and its technology stack makes it difficult to query. Access to Linked Data could be greatly facilitated if it were supported by a tool with a strong focus on usability. In this paper we present the YASGUI family of SPARQL clients, a continuation of the YASGUI tool introduced more than two years ago. The YASGUI family of SPARQL clients enables publishers to improve ease of access for their SPARQL endpoints, and gives consumers of Linked Data a robust, feature-rich and user friendly SPARQL editor. We show that the YASGUI family had significant impact on the landscape of Linked Data management: YASGUI components are integrated in state-of-the-art triple-stores and Linked Data applications, and used as front-end by a large number of Linked Data publishers. Additionally, we show that the YASGUI web service - which provides access to any SPARQL endpoint - has a large and growing user base amongst Linked Data consumers.

@inbook{6d083df218bb468baf4d48b4dc55be0b,
  title     = "A Monitoring System for the Safety of Building Structure Based on W2T Methodology",
  abstract  = "With the development of the Internet of things, monitoring systems for the safety of building structure (SBS) provide people with the important data about the main supporting points in the buildings. More and more data give the engineers an overload work problem, which can be solved by a systematic method making these monitoring systems more reliable, efficient and intelligent. Under the framework of the Wisdom Web of Things (W2T), we design a monitoring system for the SBS, by using the semantic technology. This system establishes a data cycle among the physical world (buildings), the social world (humans) and the cyber world (computers), and provides various services in the monitoring process to alleviate the engineers’ workload. In this system, the sensors which are connected via cable or wireless way, are used to monitor the different parameters of building structure. The semantic data can be obtained and represented by RDF to describe the meanings of sensor data, and can provide the application background for users. LarKC, a platform for scalable semantic data processing, is used for semantic querying about the data. Based on this uniform representation of data and semantic processing, intelligent services can be provided by the effective data analysis. This provides the possibility to integrate all of the monitoring systems for the safety of building structure in urban computing.",
  author    = "Haiyuan Wang and Zhisheng Huang and Ning Zhong and Jiajin Huang and Yuzhong Han and Feng Zhang",
  year      = "2016",
  month     = "11",
  doi       = "10.1007/978-3-319-44198-6",
  isbn      = "978-3-319-44196-2",
  pages     = "323--335",
  editor    = "Ning Zhong and Jianhua Ma and Jiming Liu and Runhe Huang and Xiaohui Tao",
  booktitle = "Wisdom Web of Things",
  publisher = "Springer",
}

With the development of the Internet of things, monitoring systems for the safety of building structure (SBS) provide people with the important data about the main supporting points in the buildings. More and more data give the engineers an overload work problem, which can be solved by a systematic method making these monitoring systems more reliable, efficient and intelligent. Under the framework of the Wisdom Web of Things (W2T), we design a monitoring system for the SBS, by using the semantic technology. This system establishes a data cycle among the physical world (buildings), the social world (humans) and the cyber world (computers), and provides various services in the monitoring process to alleviate the engineers’ workload. In this system, the sensors which are connected via cable or wireless way, are used to monitor the different parameters of building structure. The semantic data can be obtained and represented by RDF to describe the meanings of sensor data, and can provide the application background for users. LarKC, a platform for scalable semantic data processing, is used for semantic querying about the data. Based on this uniform representation of data and semantic processing, intelligent services can be provided by the effective data analysis. This provides the possibility to integrate all of the monitoring systems for the safety of building structure in urban computing.

@inbook{66b040c64f254e379e952c8ef535213a,
  title     = "Suitable Route Recommendation Inspired by Cognition",
  abstract  = "With the increasing popularity of mobile phones, large amounts of real and reliable mobile phone data are being generated every day. These mobile phone data represent the practical travel routes of users and imply the intelligence of them in selecting a suitable route. Usually, an experienced user knows which route is congested in a specified period of time but unblocked in another period of time. Moreover, a route used frequently and recently by a user is usually the suitable one to satisfy the user’s needs. ACT-R (Adaptive Control of Thought-Rational) is a computational cognitive architecture, which provides a good framework to understand the principles and mechanisms of information organization, retrieval and selection in human memory. In this chapter, we employ ACT-R to model the process of selecting a suitable route of users. We propose a cognition-inspired route recommendation method to mine the intelligence of users in selecting a suitable route, evaluate the suitability of the routes, and recommend an ordered list of routes for subscribers. Experiments show that it is effective and feasible to recommend the suitable routes inspired by cognition.",
  author    = "Hui Wang and Jiajin Huang and Erzhong Zhou and Zhisheng Huang and Ning Zhong",
  year      = "2016",
  month     = "11",
  doi       = "10.1007/978-3-319-44198-6_13",
  isbn      = "978-3-319-44196-2",
  pages     = "303--322",
  editor    = "Ning Zhong and Jianhua Ma and Jiming Liu and Runhe Huang and Xiaohui Tao",
  booktitle = "Wisdom Web of Things",
  publisher = "Springer",
}

With the increasing popularity of mobile phones, large amounts of real and reliable mobile phone data are being generated every day. These mobile phone data represent the practical travel routes of users and imply the intelligence of them in selecting a suitable route. Usually, an experienced user knows which route is congested in a specified period of time but unblocked in another period of time. Moreover, a route used frequently and recently by a user is usually the suitable one to satisfy the user’s needs. ACT-R (Adaptive Control of Thought-Rational) is a computational cognitive architecture, which provides a good framework to understand the principles and mechanisms of information organization, retrieval and selection in human memory. In this chapter, we employ ACT-R to model the process of selecting a suitable route of users. We propose a cognition-inspired route recommendation method to mine the intelligence of users in selecting a suitable route, evaluate the suitability of the routes, and recommend an ordered list of routes for subscribers. Experiments show that it is effective and feasible to recommend the suitable routes inspired by cognition.

@inbook{cdce099b6d134e23a733033930db4362,
  title     = "WaaS—Wisdom as a Service",
  abstract  = "An emerging hyper-world encompasses all human activities in a social-cyber-physical space. Its power derives from the Wisdom Web of Things (W2T) cycle, namely, “from things to data, information, knowledge, wisdom, services, humans, and then back to things.” The W2T cycle leads to a harmonious symbiosis among humans, computers and things, which can be constructed by large-scale converging of intelligent information technology applications with an open and interoperable architecture. The recent advances in cloud computing, the Internet/Web of Things, big data and other research fields have provided just such an open system architecture with resource sharing/services. The next step is therefore to develop an open and interoperable content architecture with intelligence sharing/services for the organization and transformation in the “data, information, knowledge and wisdom (DIKW)” hierarchy. This chapter introduces Wisdom as a Service (WaaS) as a content architecture based on the “paying only for what you use” IT business trend. The WaaS infrastructure, WaaS economics, and the main challenges in WaaS research and applications are discussed. A case study is described to demonstrate the usefulness and significance of WaaS. Relying on the clouds (cloud computing), things (Internet of Things) and big data, WaaS provides a practical approach to realize the W2T cycle in the hyper-world for the coming age of ubiquitous intelligent IT applications.",
  author    = "Jianhui Chen and Jianhua Ma and Ning Zhong and Yiyu Yao and Jiming Liu and Runhe Huang and Wenbin Li and Zhisheng Huang and Yang Gao",
  year      = "2016",
  month     = "11",
  doi       = "10.1007/978-3-319-44198-6_2",
  isbn      = "978-3-319-44196-2",
  pages     = "27--46",
  editor    = "Ning Zhong and Jianhua Ma and Jiming Liu and Runhe Huang and Xiaohui Tao",
  booktitle = "Wisdom Web of Things",
}

An emerging hyper-world encompasses all human activities in a social-cyber-physical space. Its power derives from the Wisdom Web of Things (W2T) cycle, namely, “from things to data, information, knowledge, wisdom, services, humans, and then back to things.” The W2T cycle leads to a harmonious symbiosis among humans, computers and things, which can be constructed by large-scale converging of intelligent information technology applications with an open and interoperable architecture. The recent advances in cloud computing, the Internet/Web of Things, big data and other research fields have provided just such an open system architecture with resource sharing/services. The next step is therefore to develop an open and interoperable content architecture with intelligence sharing/services for the organization and transformation in the “data, information, knowledge and wisdom (DIKW)” hierarchy. This chapter introduces Wisdom as a Service (WaaS) as a content architecture based on the “paying only for what you use” IT business trend. The WaaS infrastructure, WaaS economics, and the main challenges in WaaS research and applications are discussed. A case study is described to demonstrate the usefulness and significance of WaaS. Relying on the clouds (cloud computing), things (Internet of Things) and big data, WaaS provides a practical approach to realize the W2T cycle in the hyper-world for the coming age of ubiquitous intelligent IT applications.

@article{22ff35d5e5d7447b852af14f2bf7aba5,
  title   = "Linked Data for Digital History: Lessons Learned from Three Case Studies",
  author  = "{de Boer}, V. and {Merono Penuela}, A. and C.J. Ockeloen",
  year    = "2016",
  month   = "10",
  pages   = "139--162",
  journal = "Anejos de la Revista de Historiografía",
  number  = "4",
}

@article{a4774a07abff41b98843780ec580fafc,
  title     = "Special Issue: Modern Hardware",
  author    = "Peter Boncz and Wolfgang Lehner and Thomas Neumann",
  year      = "2016",
  month     = "10",
  doi       = "10.1007/s00778-016-0440-7",
  volume    = "25",
  pages     = "623--624",
  journal   = "VLDB Journal",
  issn      = "1066-8888",
  publisher = "Springer Verlag",
  number    = "5",
}

@article{8fcbb631b38f4a52999735c46e47434a,
  title     = "Predictive modeling of colorectal cancer using a dedicated pre-processing pipeline on routine electronic medical records",
  abstract  = "Over the past years, research utilizing routine care data extracted from Electronic Medical Records (EMRs) has increased tremendously. Yet there are no straightforward, standardized strategies for pre-processing these data. We propose a dedicated medical pre-processing pipeline aimed at taking on many problems and opportunities contained within EMR data, such as their temporal, inaccurate and incomplete nature. The pipeline is demonstrated on a dataset of routinely recorded data in general practice EMRs of over 260,000 patients, in which the occurrence of colorectal cancer (CRC) is predicted using various machine learning techniques (i.e., CART, LR, RF) and subsets of the data. CRC is a common type of cancer, of which early detection has proven to be important yet challenging. The results are threefold. First, the predictive models generated using our pipeline reconfirmed known predictors and identified new, medically plausible, predictors derived from the cardiovascular and metabolic disease domain, validating the pipeline's effectiveness. Second, the difference between the best model generated by the data-driven subset (AUC 0.891) and the best model generated by the current state of the art hypothesis-driven subset (AUC 0.864) is statistically significant at the 95% confidence interval level. Third, the pipeline itself is highly generic and independent of the specific disease targeted and the EMR used. In conclusion, the application of established machine learning techniques in combination with the proposed pipeline on EMRs has great potential to enhance disease prediction, and hence early detection and intervention in medical practice.",
  keywords  = "Colorectal cancer, Data mining, Data processing, Electronic medical records, Machine learning",
  author    = "Reinier Kop and Mark Hoogendoorn and {ten Teije}, Annette and Büchner, {Frederike L.} and Pauline Slottje and Moons, {Leon M G} and Numans, {Mattijs E.}",
  year      = "2016",
  month     = "9",
  doi       = "10.1016/j.compbiomed.2016.06.019",
  volume    = "76",
  pages     = "30--38",
  journal   = "Computers in Biology and Medicine",
  issn      = "0010-4825",
  publisher = "Elsevier Limited",
}

Over the past years, research utilizing routine care data extracted from Electronic Medical Records (EMRs) has increased tremendously. Yet there are no straightforward, standardized strategies for pre-processing these data. We propose a dedicated medical pre-processing pipeline aimed at taking on many problems and opportunities contained within EMR data, such as their temporal, inaccurate and incomplete nature. The pipeline is demonstrated on a dataset of routinely recorded data in general practice EMRs of over 260,000 patients, in which the occurrence of colorectal cancer (CRC) is predicted using various machine learning techniques (i.e., CART, LR, RF) and subsets of the data. CRC is a common type of cancer, of which early detection has proven to be important yet challenging. The results are threefold. First, the predictive models generated using our pipeline reconfirmed known predictors and identified new, medically plausible, predictors derived from the cardiovascular and metabolic disease domain, validating the pipeline's effectiveness. Second, the difference between the best model generated by the data-driven subset (AUC 0.891) and the best model generated by the current state of the art hypothesis-driven subset (AUC 0.864) is statistically significant at the 95% confidence interval level. Third, the pipeline itself is highly generic and independent of the specific disease targeted and the EMR used. In conclusion, the application of established machine learning techniques in combination with the proposed pipeline on EMRs has great potential to enhance disease prediction, and hence early detection and intervention in medical practice.

@inbook{81f07f72c43d466a8c80eb41d581cc13,
  title     = "VectorH: Taking SQL-on-Hadoop to the next level",
  abstract  = "Actian Vector in Hadoop (VectorH for short) is a new SQL-on-Hadoop system built on top of the fast Vectorwise analytical database system. VectorH achieves fault tolerance and storage scalability by relying on HDFS, and extends the state-of-the-art in SQL-on-Hadoop systems by instrumenting the HDFS replication policy to optimize read locality. VectorH integrates with YARN for workload management, achieving a high degree of elasticity. Even though HDFS is an append-only file-system, and VectorH supports (update-averse) ordered tables, trickle updates are possible thanks to Positional Delta Trees (PDTs), a diffferential update structure that can be queried efficiently. We describe the changes made to single-server Vectorwise to turn it into a Hadoop-based MPP system, encompassing workload management, parallel query optimization and execution, HDFS storage, transaction processing and Spark integration. We evaluate VectorH against HAWQ, Impala, SparkSQL and Hive, showing orders of magnitude better performance.",
  author    = "Andrei Costea and Adrian Ionescu and Bogdan Raducanu and Michał Świtakowski and Cristian Bârca and Juliusz Sompolski and Alicja Łuszczak and Michał Szafrański and {De Nijs}, Giel and Peter Boncz",
  year      = "2016",
  month     = "6",
  doi       = "10.1145/2882903.2903742",
  volume    = "26-June-2016",
  pages     = "1105--1117",
  booktitle = "SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data",
  publisher = "Association for Computing Machinery (ACM)",
}

Actian Vector in Hadoop (VectorH for short) is a new SQL-on-Hadoop system built on top of the fast Vectorwise analytical database system. VectorH achieves fault tolerance and storage scalability by relying on HDFS, and extends the state-of-the-art in SQL-on-Hadoop systems by instrumenting the HDFS replication policy to optimize read locality. VectorH integrates with YARN for workload management, achieving a high degree of elasticity. Even though HDFS is an append-only file-system, and VectorH supports (update-averse) ordered tables, trickle updates are possible thanks to Positional Delta Trees (PDTs), a diffferential update structure that can be queried efficiently. We describe the changes made to single-server Vectorwise to turn it into a Hadoop-based MPP system, encompassing workload management, parallel query optimization and execution, HDFS storage, transaction processing and Spark integration. We evaluate VectorH against HAWQ, Impala, SparkSQL and Hive, showing orders of magnitude better performance.

@inbook{4c8c9c9bb95a4376a43d9eaa67f7db00,
  title     = "Powerful and efficient bulk shortest-path queries: Cypher language extension & Giraph implementation",
  author    = "Peter Rutgers and Claudio Martella and Spyros Voulgaris and Peter Boncz",
  year      = "2016",
  month     = "6",
  doi       = "10.1145/2960414.2960420",
  volume    = "24-June-2016",
  booktitle = "ACM International Conference Proceeding Series",
}

@article{d35101d170504aa89903f06633f2236d,
  title     = "ClioPatria: A SWI-prolog infrastructure for the semantic web",
  abstract  = "ClioPatria is a comprehensive semantic web development framework based on SWI-Prolog. SWI-Prolog provides an efficient C-based main-memory RDF store that is designed to cooperate naturally and efficiently with Prolog, realizing a flexible RDF-based environment for rule based programming. ClioPatria extends this core with a SPARQL and LOD server, an extensible web frontend to manage the server, browse the data, query the data using SPARQL and Prolog and a Git-based plugin manager. The ability to query RDF using Prolog provides query composition and smooth integration with application logic. ClioPatria is primarily positioned as a prototyping platform for exploring novel ways of reasoning with RDF data. It has been used in several research projects in order to perform tasks such as data integration and enrichment and semantic search.",
  keywords  = "Logic programming, Semantic Web framework, Triple store",
  author    = "Jan Wielemaker and Wouter Beek and Michiel Hildebrand and {Van Ossenbruggen}, Jacco",
  year      = "2016",
  month     = "6",
  doi       = "10.3233/SW-150191",
  volume    = "7",
  pages     = "529--541",
  journal   = "Semantic Web",
  issn      = "1570-0844",
  publisher = "IOS Press",
  number    = "5",
}

ClioPatria is a comprehensive semantic web development framework based on SWI-Prolog. SWI-Prolog provides an efficient C-based main-memory RDF store that is designed to cooperate naturally and efficiently with Prolog, realizing a flexible RDF-based environment for rule based programming. ClioPatria extends this core with a SPARQL and LOD server, an extensible web frontend to manage the server, browse the data, query the data using SPARQL and Prolog and a Git-based plugin manager. The ability to query RDF using Prolog provides query composition and smooth integration with application logic. ClioPatria is primarily positioned as a prototyping platform for exploring novel ways of reasoning with RDF data. It has been used in several research projects in order to perform tasks such as data integration and enrichment and semantic search.

@article{e1ddb0419e3f41a181b986d27ca0553e,
  title     = "Bitwise dimensional co-clustering for analytical workloads",
  abstract  = "Analytical workloads in data warehouses often include heavy joins where queries involve multiple fact tables in addition to the typical star-patterns, dimensional grouping and selections. In this paper we propose a new processing and storage framework called bitwise dimensional co-clustering (BDCC) that avoids replication and thus keeps updates fast, yet is able to accelerate all these foreign key joins, efficiently support grouping and pushes down most dimensional selections. The core idea of BDCC is to cluster each table on a mix of dimensions, each possibly derived from attributes imported over an incoming foreign key and this way creating foreign key connected tables with partially shared clusterings. These are later used to accelerate any join between two tables that have some dimension in common and additionally permit to push down and propagate selections (reduce I/O) and accelerate aggregation and ordering operations. Besides the general framework, we describe an algorithm to derive such a physical co-clustering database automatically and describe query processing and query optimization techniques that can easily be fitted into existing relational engines. We present an experimental evaluation on the TPC-H benchmark in the Vectorwise system, showing that co-clustering can significantly enhance its already high performance and at the same time significantly reduce the memory consumption of the system.",
  keywords  = "Clustering, Data warehouse, Database design, Indexing, OLAP, Query processing, Sandwich operators, Storage",
  author    = "Stephan Baumann and Peter Boncz and Sattler, {Kai Uwe}",
  year      = "2016",
  month     = "6",
  doi       = "10.1007/s00778-015-0417-y",
  volume    = "25",
  pages     = "291--316",
  journal   = "VLDB Journal",
  issn      = "1066-8888",
  publisher = "Springer Verlag",
  number    = "3",
}

Analytical workloads in data warehouses often include heavy joins where queries involve multiple fact tables in addition to the typical star-patterns, dimensional grouping and selections. In this paper we propose a new processing and storage framework called bitwise dimensional co-clustering (BDCC) that avoids replication and thus keeps updates fast, yet is able to accelerate all these foreign key joins, efficiently support grouping and pushes down most dimensional selections. The core idea of BDCC is to cluster each table on a mix of dimensions, each possibly derived from attributes imported over an incoming foreign key and this way creating foreign key connected tables with partially shared clusterings. These are later used to accelerate any join between two tables that have some dimension in common and additionally permit to push down and propagate selections (reduce I/O) and accelerate aggregation and ordering operations. Besides the general framework, we describe an algorithm to derive such a physical co-clustering database automatically and describe query processing and query optimization techniques that can easily be fitted into existing relational engines. We present an experimental evaluation on the TPC-H benchmark in the Vectorwise system, showing that co-clustering can significantly enhance its already high performance and at the same time significantly reduce the memory consumption of the system.

@article{283d4b8ef23a46bb9ecc246cfc723196,
  title     = "How organisation of architecture documentation affects architectural knowledge retrieval",
  abstract  = "A common approach to software architecture documentation in industry projects is the use of file-based documents. This approach offers a single-dimensional arrangement of the architectural knowledge. Knowledge retrieval from file-based architecture documentation is efficient if the organisation of knowledge supports the needs of the readers; otherwise it can be difficult. In this paper, we compare the organisation and retrieval of architectural knowledge in a file-based documentation approach and an ontology-based documentation approach. The ontology-based approach offers a multi-dimensional organisation of architectural knowledge by means of a software ontology and semantic wiki, whereas file-based documentation typically uses hierarchical organisation by directory structure and table of content. We conducted case studies in two companies to study the efficiency and effectiveness of retrieving architectural knowledge from the different organisations of knowledge. We found that the use of better knowledge organisation correlates with the efficiency and effectiveness of AK retrieval. Professionals who used the knowledge organisation found this beneficial.",
  keywords  = "Software architecture documentation, software architectural knowledge, architectural knowledge retrieval, software ontologies, semantic wiki, ontology-based documentation",
  author    = "{de Graaf}, K.A. and P. Liang and A. Tang and J.C. Vliet",
  year      = "2016",
  month     = "6",
  doi       = "10.1016/j.scico.2015.10.014",
  volume    = "121",
  pages     = "75--99",
  journal   = "Science of Computer Programming",
  issn      = "0167-6423",
  publisher = "Elsevier",
}

A common approach to software architecture documentation in industry projects is the use of file-based documents. This approach offers a single-dimensional arrangement of the architectural knowledge. Knowledge retrieval from file-based architecture documentation is efficient if the organisation of knowledge supports the needs of the readers; otherwise it can be difficult. In this paper, we compare the organisation and retrieval of architectural knowledge in a file-based documentation approach and an ontology-based documentation approach. The ontology-based approach offers a multi-dimensional organisation of architectural knowledge by means of a software ontology and semantic wiki, whereas file-based documentation typically uses hierarchical organisation by directory structure and table of content. We conducted case studies in two companies to study the efficiency and effectiveness of retrieving architectural knowledge from the different organisations of knowledge. We found that the use of better knowledge organisation correlates with the efficiency and effectiveness of AK retrieval. Professionals who used the knowledge organisation found this beneficial.

@article{9254330a91e749cdb89b65db174225b4,
  title     = "LOD Laundromat: Why the Semantic Web needs centralization (even if we don't like it)",
  abstract  = "LOD Laundromat poses a centralized solution for today's Semantic Web problems. This approach adheres more closely to the original vision of a Web of Data, providing uniform access to a large and ever-increasing subcollection of the LOD Cloud.",
  keywords  = "Internet/Web technologies, Linked Data, Linked Open Data, LOD, Semantic Web",
  author    = "Wouter Beek and Laurens Rietveld and Stefan Schlobach and {van Harmelen}, Frank",
  year      = "2016",
  month     = "3",
  doi       = "10.1109/MIC.2016.43",
  volume    = "20",
  pages     = "78--81",
  journal   = "IEEE Internet Computing",
  issn      = "1089-7801",
  publisher = "Institute of Electrical and Electronics Engineers Inc.",
  number    = "2",
}

LOD Laundromat poses a centralized solution for today's Semantic Web problems. This approach adheres more closely to the original vision of a Web of Data, providing uniform access to a large and ever-increasing subcollection of the LOD Cloud.

@inbook{e7e1e9893fe14b078edbf1779f4b1c68,
  title     = "A Contextualised Semantics for owl: sameAs",
  abstract  = "Identity relations are at the foundation of the Semantic Web and the Linked Data Cloud. In many instances the classical interpretation of identity is too strong for practical purposes. This is particularly the case when two entities are considered the same in some but not all contexts. Unfortunately, modeling the specific contexts in which an identity relation holds is cumbersome and, due to arbitrary reuse and the Open World Assumption, it is impossible to anticipate all contexts in which an entity will be used. We propose an alternative semantics for owl:sameAs that partitions the original relation into a hierarchy of subrelations. The subrelation to which an identity statement belongs depends on the dataset in which the statement occurs. Adding future assertions may change the subrelation to which an identity statement belongs, resulting in a context-dependent and non-monotonic semantics. We show that this more fine-grained semantics is better able to characterize the actual use of owl:sameAs as observed in Linked Open Datasets.",
  author    = "W.G.J. Beek and K.S. Schlobach and {van Harmelen}, F.A.H.",
  year      = "2016",
  doi       = "10.1007/978-3-319-34129-3_25",
  isbn      = "9783319341286",
  volume    = "9678",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "405--419",
  booktitle = "The Semantic Web. Latest Advances and New Domains - 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29 - June 2, 2016, Proceedings",
}

Identity relations are at the foundation of the Semantic Web and the Linked Data Cloud. In many instances the classical interpretation of identity is too strong for practical purposes. This is particularly the case when two entities are considered the same in some but not all contexts. Unfortunately, modeling the specific contexts in which an identity relation holds is cumbersome and, due to arbitrary reuse and the Open World Assumption, it is impossible to anticipate all contexts in which an entity will be used. We propose an alternative semantics for owl:sameAs that partitions the original relation into a hierarchy of subrelations. The subrelation to which an identity statement belongs depends on the dataset in which the statement occurs. Adding future assertions may change the subrelation to which an identity statement belongs, resulting in a context-dependent and non-monotonic semantics. We show that this more fine-grained semantics is better able to characterize the actual use of owl:sameAs as observed in Linked Open Datasets.

@inbook{3545ab263cc3471aace89f0a628dd12d,
  title     = "Adaptive delineation of functional urban areas using Linked Open Data.",
  author    = "A. Khalili and {van den Besselaar}, P.A.A.",
  year      = "2016",
  pages     = "72",
  booktitle = "RISIS Deliverable 9.2",
  publisher = "SNI",
}

@inbook{cec94fa27b524331a0c5f29e4d4c28c9,
  title     = "Adaptive Linked Data-Driven Web Components: Building Flexible and Reusable Semantic Web Interfaces - Building Flexible and Reusable Semantic Web Interfaces",
  abstract  = "Due to the increasing amount of Linked Data openly published on the Web, user-facing Linked Data Applications (LDAs) are gaining momentum. One of the major entrance barriers for Web developers to contribute to this wave of LDAs is the required knowledge of Semantic Web (SW) technologies such as the RDF data model and SPARQL query language. This paper presents an adaptive component-based approach together with its open source implementation for creating flexible and reusable SW interfaces driven by Linked Data. Linked Data-driven (LD-R) Web components abstract the complexity of the underlying SW technologies in order to allow reuse of existing Web components in LDAs, enabling Web developers who are not experts in SW to develop interfaces that view, edit and browse Linked Data. In addition to the modularity provided by the LD-R components, the proposed RDF-based configuration method allows application assemblers to reshape their user interface for different use cases, by either reusing existing shared configurations or by creating their proprietary configurations.",
  author    = "Ali Khalili and A. Loizou and {van Harmelen}, F.A.H.",
  year      = "2016",
  doi       = "10.1007/978-3-319-34129-3_41",
  isbn      = "978-3-319-34128-6",
  volume    = "9678",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer",
  pages     = "677--692",
  booktitle = "The Semantic Web. Latest Advances and New Domains - 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29 - June 2, 2016, Proceedings",
}

Due to the increasing amount of Linked Data openly published on the Web, user-facing Linked Data Applications (LDAs) are gaining momentum. One of the major entrance barriers for Web developers to contribute to this wave of LDAs is the required knowledge of Semantic Web (SW) technologies such as the RDF data model and SPARQL query language. This paper presents an adaptive component-based approach together with its open source implementation for creating flexible and reusable SW interfaces driven by Linked Data. Linked Data-driven (LD-R) Web components abstract the complexity of the underlying SW technologies in order to allow reuse of existing Web components in LDAs, enabling Web developers who are not experts in SW to develop interfaces that view, edit and browse Linked Data. In addition to the modularity provided by the LD-R components, the proposed RDF-based configuration method allows application assemblers to reshape their user interface for different use cases, by either reusing existing shared configurations or by creating their proprietary configurations.

@misc{ca63a27c612148d2bf29a6b1283c8e58,
  title  = "A Deep Neural Network for Link Prediction on Knowledge Graphs",
  author = "W.X. Wilcke and {de Boer}, V. and {van Harmelen}, F.A.H. and {de Kleijn}, M.T.M.",
  year   = "2016",
}

@article{e3f25d50b5a44b00a2906e5dd944c2a9,
  title     = "An ecosystem for Linked Humanities Data",
  abstract  = "The main promise of the digital humanities is the ability to perform scholar studies at a much broader scale, and in a much more reusable fashion. The key enabler for such studies is the availability of sufficiently well described data. For the field of socio-economic history, data usually comes in a tabular form. Existing efforts to curate and publish datasets take a top-down approach and are focused on large collections. This paper presents QBer and the underlying structured datahub, which address the long tail of research data by catering for the needs of individual scholars. QBer allows researchers to publish their (small) datasets, link them to existing vocabularies and other datasets, and thereby contribute to a growing collection of interlinked datasets. We present QBer, and evaluate our first results by showing how our system facilitates two use cases in socio-economic history.",
  keywords  = "digital humanities, structured data, linked data, QBer",
  author    = "Rinke Hoekstra and Albert Meroño-Peñuela and Kathrin Dentler and Auke Rijpma and Richard Zijdeman and Ivo Zandhuis",
  year      = "2016",
  doi       = "10.1007/978-3-319-47602-5_54",
  volume    = "1608",
  pages     = "85--96",
  journal   = "CEUR workshop proceedings",
  issn      = "1613-0073",
  publisher = "CEUR Workshop Proceedings",
}

The main promise of the digital humanities is the ability to perform scholar studies at a much broader scale, and in a much more reusable fashion. The key enabler for such studies is the availability of sufficiently well described data. For the field of socio-economic history, data usually comes in a tabular form. Existing efforts to curate and publish datasets take a top-down approach and are focused on large collections. This paper presents QBer and the underlying structured datahub, which address the long tail of research data by catering for the needs of individual scholars. QBer allows researchers to publish their (small) datasets, link them to existing vocabularies and other datasets, and thereby contribute to a growing collection of interlinked datasets. We present QBer, and evaluate our first results by showing how our system facilitates two use cases in socio-economic history.

@inbook{bd128ebb57df4bfdb31cc455c81cdbc6,
  title     = "Are names meaningful? Quantifying social meaning on the semantic web",
  abstract  = "According to its model-theoretic semantics, Semantic Web IRIs are individual constants or predicate letters whose names are chosen arbitrarily and carry no formal meaning. At the same time it is a well-known aspect of Semantic Web pragmatics that IRIs are often constructed mnemonically, in order to be meaningful to a human interpreter. The latter has traditionally been termed ‘social meaning’, a concept that has been discussed but not yet quantitatively studied by the Semantic Web community. In this paper we use measures of mutual information content and methods from statistical model learning to quantify the meaning that is (at least) encoded in Semantic Web names. We implement the approach and evaluate it over hundreds of thousands of datasets in order to illustrate its efficacy. Our experiments confirm that many Semantic Web names are indeed meaningful and, more interestingly, we provide a quantitative lower bound on how much meaning is encoded in names on a per-dataset basis. To our knowledge, this is the first paper about the interaction between social and formal meaning, as well as the first paper that uses statistical model learning as a method to quantify meaning in the Semantic Web context. These insights are useful for the design of a new generation of Semantic Web tools that take such social meaning into account.",
  author    = "{de Rooij}, Steven and Wouter Beek and Peter Bloem and {van Harmelen}, Frank and Stefan Schlobach",
  year      = "2016",
  doi       = "10.1007/978-3-319-46523-4_12",
  isbn      = "978-3-319-46522-7",
  volume    = "9981 LNCS",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "184--199",
  booktitle = "The Semantic Web - 15th International Semantic Web Conference, ISWC 2016, Proceedings",
}

According to its model-theoretic semantics, Semantic Web IRIs are individual constants or predicate letters whose names are chosen arbitrarily and carry no formal meaning. At the same time it is a well-known aspect of Semantic Web pragmatics that IRIs are often constructed mnemonically, in order to be meaningful to a human interpreter. The latter has traditionally been termed ‘social meaning’, a concept that has been discussed but not yet quantitatively studied by the Semantic Web community. In this paper we use measures of mutual information content and methods from statistical model learning to quantify the meaning that is (at least) encoded in Semantic Web names. We implement the approach and evaluate it over hundreds of thousands of datasets in order to illustrate its efficacy. Our experiments confirm that many Semantic Web names are indeed meaningful and, more interestingly, we provide a quantitative lower bound on how much meaning is encoded in names on a per-dataset basis. To our knowledge, this is the first paper about the interaction between social and formal meaning, as well as the first paper that uses statistical model learning as a method to quantify meaning in the Semantic Web context. These insights are useful for the design of a new generation of Semantic Web tools that take such social meaning into account.

@article{39ec163cbc9a41078f10c194ebcd2f3e,
  title     = "A Task-based Comparison of Linguistic and Semantic Document Retrieval Methods in the Medical Domain",
  abstract  = "Text-based and semantics-based methods are both studied intensively as methods for document retrieval. In order to gain insight in the respective merits of these two approaches, we have performed a controlled experiment where we executed a real-life task using both textbased and semantics-based techniques. To maximise the lessons that we could draw about the two approaches, we have performed an experiment where we used the same task (searching papers from the scientific literature needed for updating a medical guideline), the same test-case (updating the 2004 Dutch national breast-cancer guideline), the same gold standard (the updated 2012 Dutch national breast-cancer guideline) and the same corpus (PubMed). We then performed this task using two different methods: retrieving papers based on keywords (text-based approach) and retrieving papers based on semantic annotations (semantics-based approach). Based on this experiment, we discuss the insights that we gained from this dual set of experiments.",
  keywords  = "Concept-based search, Document retrieval, Keyword search, Relation-based search, Semantic annotation",
  author    = "Mohammad Shafahi and Qing Hu and Hamideh Afsarmanesh and Z. Huang and {ten Teije}, A.C.M. and {van Harmelen}, F.A.H.",
  year      = "2016",
  volume    = "1613",
  journal   = "CEUR workshop proceedings",
  issn      = "1613-0073",
  publisher = "CEUR Workshop Proceedings",
}

Text-based and semantics-based methods are both studied intensively as methods for document retrieval. In order to gain insight in the respective merits of these two approaches, we have performed a controlled experiment where we executed a real-life task using both textbased and semantics-based techniques. To maximise the lessons that we could draw about the two approaches, we have performed an experiment where we used the same task (searching papers from the scientific literature needed for updating a medical guideline), the same test-case (updating the 2004 Dutch national breast-cancer guideline), the same gold standard (the updated 2012 Dutch national breast-cancer guideline) and the same corpus (PubMed). We then performed this task using two different methods: retrieving papers based on keywords (text-based approach) and retrieving papers based on semantic annotations (semantics-based approach). Based on this experiment, we discuss the insights that we gained from this dual set of experiments.

@inbook{91383e82af8f4f5c82634e58bfdbf443,
  title     = "A topic-centric approach to detecting new evidences for evidence-based medical guidelines",
  abstract  = "Evidence-based Medical guidelines are developed based on the best available evidence in biomedical science and clinical practice. Such evidence-based medical guidelines should be regularly updated, so that they can optimally serve medical practice by using the latest evidence from medical research. The usual approach to detect such new evidence is to use a set of terms from a guideline recommendation and to create queries for a biomedical search engine such as PubMed, with a ranking over a selected subset of terms to search for relevant new evidence. However, the terms that appear in a guideline recommendation do not always cover all of the information we need for the search, because the contextual information (e.g. time and location, user profile, topics) is usually missing in a guideline recommendation. Enhancing the search terms with contextual information would improve the quality of the search results. In this paper, we propose a topic-centric approach to detect new evidence for updating evidence-based medical guidelines as a context-aware method to improve the search. Our experiments show that this topic centric approach can find the goal evidence for 12 guideline statements out of 16 in our test set, compared with only 5 guideline statements that were found by using a non-topic centric approach.",
  keywords  = "Context-awareness, Evidence-based medical guidelines, Medical guideline update, Semantic distance, Topic-centric approach",
  author    = "Qing Hu and Zisheng Huang and {ten Teije}, Annette and {van Harmelen}, Frank and Marshall, {M. Scott} and Andre Dekker",
  year      = "2016",
  pages     = "282--289",
  booktitle = "HEALTHINF 2016 - 9th International Conference on Health Informatics, Proceedings; Part of 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016",
  publisher = "SciTePress",
}

Evidence-based Medical guidelines are developed based on the best available evidence in biomedical science and clinical practice. Such evidence-based medical guidelines should be regularly updated, so that they can optimally serve medical practice by using the latest evidence from medical research. The usual approach to detect such new evidence is to use a set of terms from a guideline recommendation and to create queries for a biomedical search engine such as PubMed, with a ranking over a selected subset of terms to search for relevant new evidence. However, the terms that appear in a guideline recommendation do not always cover all of the information we need for the search, because the contextual information (e.g. time and location, user profile, topics) is usually missing in a guideline recommendation. Enhancing the search terms with contextual information would improve the quality of the search results. In this paper, we propose a topic-centric approach to detect new evidence for updating evidence-based medical guidelines as a context-aware method to improve the search. Our experiments show that this topic centric approach can find the goal evidence for 12 guideline statements out of 16 in our test set, compared with only 5 guideline statements that were found by using a non-topic centric approach.

@inbook{489b9422588546ec9f2a8c9c806559fd,
  title     = "Exploiting emergent schemas to make RDF systems more efficient",
  abstract  = "We build on our earlier finding that more than 95% of the triples in actual RDF triple graphs have a remarkably tabular structure, whose schema does not necessarily follow from explicit metadata such as ontologies, but for which an RDF store can automatically derive by looking at the data using so-called “emergent schema” detection techniques. In this paper we investigate how computers and in particular RDF stores can take advantage from this emergent schema to more compactly store RDF data and more efficiently optimize and execute SPARQL queries. To this end, we contribute techniques for efficient emergent schema aware RDF storage and new query operator algorithms for emergent schema aware scans and joins. In all, these techniques allow RDF schema processors fully catch up with relational database techniques in terms of rich physical database design options and efficiency, without requiring a rigid upfront schema structure definition.",
  author    = "Pham, {Minh Duc} and Peter Boncz",
  year      = "2016",
  doi       = "10.1007/978-3-319-46523-4_28",
  isbn      = "9783319465227",
  volume    = "9981 LNCS",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "463--479",
  booktitle = "The Semantic Web - 15th International Semantic Web Conference, ISWC 2016, Proceedings",
}

We build on our earlier finding that more than 95% of the triples in actual RDF triple graphs have a remarkably tabular structure, whose schema does not necessarily follow from explicit metadata such as ontologies, but for which an RDF store can automatically derive by looking at the data using so-called “emergent schema” detection techniques. In this paper we investigate how computers and in particular RDF stores can take advantage from this emergent schema to more compactly store RDF data and more efficiently optimize and execute SPARQL queries. To this end, we contribute techniques for efficient emergent schema aware RDF storage and new query operator algorithms for emergent schema aware scans and joins. In all, these techniques allow RDF schema processors fully catch up with relational database techniques in terms of rich physical database design options and efficiency, without requiring a rigid upfront schema structure definition.

@inbook{82b812259aed4390bb18653971bd6591,
  title     = "Generalizing the Detection of Internal and External Interactions in Clinical Guidelines",
  abstract  = "This paper presents a method for formally representing Computer-Interpretable Guidelines to deal with multimorbidity. Although some approaches for merging guidelines exist, improvements are still required for combining several sources of information and coping with possibly conflicting pieces of evidence coming from clinical studies. Our main contribution is twofold: (i) we provide general models and rules for representing guidelines that expresses evidence as causation beliefs; (ii) we introduce a mechanism to exploit external medical knowledge acquired from Linked Open Data (Drugbank, Sider, DIKB) to detect potential interactions between recommendations. We apply this framework to merge three guidelines (Osteoarthritis, Diabetes, and Hypertension) in order to illustrate the capability of this approach for detecting potential conflicts between guidelines and eventually propose alternatives.",
  keywords  = "Clinical guidelines, Knowledge representation, Ontologies, Semantic web",
  author    = "{Carretta Zamborlini}, Veruska and Rinke Hoekstra and {Da Silveira}, Marcos and Cedric Pruski and {ten Teije}, Annette and {van Harmelen}, Frank",
  year      = "2016",
  doi       = "10.5220/0005704101050116",
  pages     = "105--116",
  booktitle = "HEALTHINF 2016 - 9th International Conference on Health Informatics, Proceedings; Part of 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016",
  publisher = "SciTePress",
}

This paper presents a method for formally representing Computer-Interpretable Guidelines to deal with multimorbidity. Although some approaches for merging guidelines exist, improvements are still required for combining several sources of information and coping with possibly conflicting pieces of evidence coming from clinical studies. Our main contribution is twofold: (i) we provide general models and rules for representing guidelines that expresses evidence as causation beliefs; (ii) we introduce a mechanism to exploit external medical knowledge acquired from Linked Open Data (Drugbank, Sider, DIKB) to detect potential interactions between recommendations. We apply this framework to merge three guidelines (Osteoarthritis, Diabetes, and Hypertension) in order to illustrate the capability of this approach for detecting potential conflicts between guidelines and eventually propose alternatives.

@article{6dc9278699694bd4b43db86691dfa64c,
  title     = "grlc Makes GitHub Taste Like Linked Data APIs",
  abstract  = "Building Web APIs on top of SPARQL endpoints is becoming a common practice to enable universal access to the integration favorable dataspace of Linked Data. However, the Linked Data community cannot expect users to learn SPARQL to query this dataspace, and Web APIs are the most common way of enabling programmatic access to data on the Web. However, the implementation of Web APIs around Linked Data is often a tedious and repetitive process. Recent work speeds up thisLinked Data API construction by wrapping it around SPARQL queries, which carry out the API functionality under the hood. Inspired by this, in this paper we present grlc, a lightweight server that translates SPARQL queries curated in GitHub repositories to Linked Data APIs on the fly.",
  keywords  = "Git, GitHub, Linked data APIs, SPARQL",
  author    = "Albert Meroño-Peñuela and Rinke Hoekstra",
  year      = "2016",
  doi       = "10.1007/978-3-319-47602-5_48",
  volume    = "1629",
  journal   = "CEUR workshop proceedings",
  issn      = "1613-0073",
  publisher = "CEUR Workshop Proceedings",
}

Building Web APIs on top of SPARQL endpoints is becoming a common practice to enable universal access to the integration favorable dataspace of Linked Data. However, the Linked Data community cannot expect users to learn SPARQL to query this dataspace, and Web APIs are the most common way of enabling programmatic access to data on the Web. However, the implementation of Web APIs around Linked Data is often a tedious and repetitive process. Recent work speeds up thisLinked Data API construction by wrapping it around SPARQL queries, which carry out the API functionality under the hood. Inspired by this, in this paper we present grlc, a lightweight server that translates SPARQL queries curated in GitHub repositories to Linked Data APIs on the fly.

@inbook{44bee764c491494189957818fb36f123,
  title     = "grlc Makes GitHub Taste Like Linked Data APIs",
  abstract  = "Building Web APIs on top of SPARQL endpoints is becoming common practice. It enables universal access to the integration favorable data space of Linked Data. In the majority of use cases, users cannot be expected to learn SPARQL to query this data space. Web APIs are the most common way to enable programmatic access to data on the Web. However, the implementation of Web APIs around Linked Data is often a tedious and repetitive process. Recent work speeds up this Linked Data API construction by wrapping it around SPARQL queries, which carry out the API functionality under the hood. Inspired by this development, in this paper we present grlc, a lightweight server that takes SPARQL queries curated in GitHub repositories, and translates them to Linked Data APIs on the fly.",
  keywords  = "Git, GitHub, Linked data APIs, RESTFul, SPARQL",
  author    = "{Merono Penuela}, A. and R.J. Hoekstra",
  year      = "2016",
  doi       = "10.1007/978-3-319-47602-5_48",
  isbn      = "9783319476018",
  volume    = "9989 LNCS",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "342--353",
  booktitle = "The Semantic Web - ESWC 2016 Satellite Events, Revised Selected Papers",
}

Building Web APIs on top of SPARQL endpoints is becoming common practice. It enables universal access to the integration favorable data space of Linked Data. In the majority of use cases, users cannot be expected to learn SPARQL to query this data space. Web APIs are the most common way to enable programmatic access to data on the Web. However, the implementation of Web APIs around Linked Data is often a tedious and repetitive process. Recent work speeds up this Linked Data API construction by wrapping it around SPARQL queries, which carry out the API functionality under the hood. Inspired by this development, in this paper we present grlc, a lightweight server that takes SPARQL queries curated in GitHub repositories, and translates them to Linked Data APIs on the fly.

@inbook{84bd5d6049614c52b3d233eeff1f1c76,
  title     = "How Good Are Query Optimizers, Really?",
  abstract  = "Finding a good join order is crucial for query performance. In this paper, we introduce the Join Order Benchmark (JOB) and experimentally revisit the main components in the classic query optimizer architecture using a complex, real-world data set and realistic multi-join queries. We investigate the quality of industrial-strength cardinality estimators and find that all estimators routinely produce large errors. We further show that while estimates are essential for finding a good join order, query performance is unsatisfactory if the query engine relies too heavily on these estimates. Using another set of experiments that measure the impact of the cost model, we find that it has much less influence on query performance than the cardinality estimates. Finally, we investigate plan enumeration techniques comparing exhaustive dynamic programming with heuristic algorithms and find that exhaustive enumeration improves performance despite the sub-optimal cardinality estimates.",
  author    = "Viktor Leis and Andrey Gubichev and Atanas Mirchev and Peter Boncz and Alfons Kemper and Thomas Neumann",
  year      = "2016",
  volume    = "9",
  pages     = "204--215",
  booktitle = "Proceedings of the VLDB Endowment",
  publisher = "Association for Computing Machinery (ACM)",
  edition   = "3",
}

Finding a good join order is crucial for query performance. In this paper, we introduce the Join Order Benchmark (JOB) and experimentally revisit the main components in the classic query optimizer architecture using a complex, real-world data set and realistic multi-join queries. We investigate the quality of industrial-strength cardinality estimators and find that all estimators routinely produce large errors. We further show that while estimates are essential for finding a good join order, query performance is unsatisfactory if the query engine relies too heavily on these estimates. Using another set of experiments that measure the impact of the cost model, we find that it has much less influence on query performance than the cardinality estimates. Finally, we investigate plan enumeration techniques comparing exhaustive dynamic programming with heuristic algorithms and find that exhaustive enumeration improves performance despite the sub-optimal cardinality estimates.

@article{4d178cf0055f4dd1acf70f17b8d01a25,
  title     = "Inferring recommendation interactions in clinical guidelines",
  abstract  = "The formal representation of clinical knowledge is still an open research topic. Classical representation languages for clinical guidelines are used to produce diagnostic and treatment plans. However, they have important limitations, e.g. when looking for ways to re-use, combine, and reason over existing clinical knowledge. These limitations are especially problematic in the context of multimorbidity; patients that suffer from multiple diseases. To overcome these limitations, this paper proposes a model for clinical guidelines (TMR4I) that allows the re-use and combination of knowledge from multiple guidelines. Semantic Web technology is applied to implement the model, allowing us to automatically infer interactions between recommendations, such as recommending the same drug more than once. It relies on an existing Linked Data set, DrugBank, for identifying drug-drug interactions. We evaluate the model by applying it to two realistic case studies on multimorbidity that combine guidelines for two (Duodenal Ulcer and Transient Ischemic Attack) and three diseases (Osteoarthritis, Hypertension and Diabetes) and compare the results with existing methods.",
  keywords  = "Clinical knowledge representation, OWL, SPARQL, SWRL, combining medical guidelines, multimorbidity, reasoning, rules",
  author    = "{Carretta Zamborlini}, Veruska and Rinke Hoekstra and {Da Silveira}, Marcos and Cedric Pruski and {ten Teije}, A.C.M. and {van Harmelen}, F.A.H.",
  year      = "2016",
  doi       = "10.3233/SW-150212",
  volume    = "7",
  pages     = "421--446",
  journal   = "Semantic Web",
  issn      = "1570-0844",
  publisher = "IOS Press",
  number    = "4",
}

The formal representation of clinical knowledge is still an open research topic. Classical representation languages for clinical guidelines are used to produce diagnostic and treatment plans. However, they have important limitations, e.g. when looking for ways to re-use, combine, and reason over existing clinical knowledge. These limitations are especially problematic in the context of multimorbidity; patients that suffer from multiple diseases. To overcome these limitations, this paper proposes a model for clinical guidelines (TMR4I) that allows the re-use and combination of knowledge from multiple guidelines. Semantic Web technology is applied to implement the model, allowing us to automatically infer interactions between recommendations, such as recommending the same drug more than once. It relies on an existing Linked Data set, DrugBank, for identifying drug-drug interactions. We evaluate the model by applying it to two realistic case studies on multimorbidity that combine guidelines for two (Duodenal Ulcer and Transient Ischemic Attack) and three diseases (Osteoarthritis, Hypertension and Diabetes) and compare the results with existing methods.

@misc{60d8c039cc9e446685f5a19939709225,
  title  = "Integrating Diachronous Conceptual Lexicons through Linked Open Data",
  author = "E. Maks and {van Erp}, M.G.J. and P.T.J.M. Vossen and R.J. Hoekstra and {van der Sijs}, N.",
  note   = "Proceedings title: DHBenelux Place of publication: Luxembourg",
  year   = "2016",
}

@book{8575d1a8fc274801afefe62bcdff596a,
  title     = "Interim report on the disambiguation results: RISIS Deliverable D25.1",
  author    = "Stefan Schlobach and O.A.K. Idrissou and R.J. Hoekstra and A. Khalili and {van Harmelen}, Frank and {van den Besselaar}, P.A.A.",
  year      = "2016",
  publisher = "Vrije Universiteit",
}

@misc{a0ef482c38564118959ba0dac4312ad3,
  title  = "Kasadaka: a rapid prototyping platform for the rural poor",
  author = "Lo, {A. Gossa} and K.S. Schlobach and {de Boer}, V.",
  year   = "2016",
}

@inbook{210cc82a653b4c559a54a863de63cad2,
  title     = "Knowledge Services Using Rule-Based Formalization for Eligibility Criteria of Clinical Trials",
  abstract  = "Rule-based formalization of eligibility criteria in clinical trials have distinguished features such as declaration, easy maintenance, reusability, and expressiveness. In this paper, we present several knowledge services which can be provided by the rule-based formalization of eligibility criteria. The rule-based formalization can be generated automatically by using the logic programming Prolog with the support of NLP tools for the semantic annotation and relation extraction with medical ontologies/terminologies such as UMLS and SNOMED CT. We show how those automatically generated rule-based formalization for eligibility criteria can be used for the patient recruitment service in SemanticCT, a semantically-enabled system for clinical trials.",
  author    = "Z. Huang and Q. Hu and {ten Teije}, A.C.M. and {van Harmelen}, F.A.H. and S. Ait-Mokhtar",
  year      = "2016",
  doi       = "10.1007/978-3-319-48335-1_6",
  isbn      = "9783319483344",
  volume    = "10038 LNCS",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "49--61",
  booktitle = "Health Information Science - 5th International Conference, HIS 2016, Proceedings",
}

Rule-based formalization of eligibility criteria in clinical trials have distinguished features such as declaration, easy maintenance, reusability, and expressiveness. In this paper, we present several knowledge services which can be provided by the rule-based formalization of eligibility criteria. The rule-based formalization can be generated automatically by using the logic programming Prolog with the support of NLP tools for the semantic annotation and relation extraction with medical ontologies/terminologies such as UMLS and SNOMED CT. We show how those automatically generated rule-based formalization for eligibility criteria can be used for the patient recruitment service in SemanticCT, a semantically-enabled system for clinical trials.

@article{f4c2294d2bb6422bb192e86078f68a6d,
  title     = "LDBC Graphalytics: A Benchmark for Large-Scale Graph Analysis on Parallel and Distributed Platforms",
  abstract  = "In this paper we introduce LDBC Graphalytics, a new industrial-grade benchmark for graph analysis platforms. It consists of six deterministic algorithms, standard datasets, synthetic dataset generators, and reference output, that enable the objective comparison of graph analysis platforms. Its test harness produces deep metrics that quantify multiple kinds of system scalability, such as horizontal/vertical and weak/strong, and of robustness, such as failures and performance variability. The benchmark comes with open-source software for generating data and monitoring performance. We describe and analyze six implementations of the benchmark (three from the community, three from the industry), providing insights into the strengths and weaknesses of the platforms. Key to our contribution, vendors perform the tuning and benchmarking of their platforms.",
  author    = "Alexandru Iosup and Tim Hegeman and Ngai, {Wing Lung} and Stijn Heldens and Arnau Prat-Pérez and Thomas Manhardt and Hassan Chafi and Mihai Capota and Narayanan Sundaram and Anderson, {Michael J.} and Tanase, {Ilie Gabriel} and Yinglong Xia and Lifeng Nai and Boncz, {Peter A.}",
  year      = "2016",
  doi       = "10.14778/3007263.3007270",
  volume    = "9",
  pages     = "1317--1328",
  journal   = "PVLDB",
  issn      = "2150-8097",
  publisher = "Very Large Data Base Endowment Inc.",
  number    = "13",
}

In this paper we introduce LDBC Graphalytics, a new industrial-grade benchmark for graph analysis platforms. It consists of six deterministic algorithms, standard datasets, synthetic dataset generators, and reference output, that enable the objective comparison of graph analysis platforms. Its test harness produces deep metrics that quantify multiple kinds of system scalability, such as horizontal/vertical and weak/strong, and of robustness, such as failures and performance variability. The benchmark comes with open-source software for generating data and monitoring performance. We describe and analyze six implementations of the benchmark (three from the community, three from the industry), providing insights into the strengths and weaknesses of the platforms. Key to our contribution, vendors perform the tuning and benchmarking of their platforms.

@article{2405656e886645b996d943e2e03a1801,
  title     = "Linked Data Reactor: a Framework for Building Reactive Linked Data Applications",
  abstract  = "This paper presents Linked Data Reactor (LD-Reactor or LD-R) as a framework for developing exible and reusable User Interface components for Linked Data applications. LD-Reactor utilizes Facebook's ReactJS components, Flux architecture and Yahoo's Fluxible framework for isomorphic Web applications. It also exploits Semantic-UI framework for exible UI themes. LD-R aims to apply the idea of component-based application development into RDF data model hence enhancing current user interfaces to view, browse and edit Linked Data.",
  author    = "Ali Khalili",
  year      = "2016",
  volume    = "1615",
  journal   = "CEUR workshop proceedings",
  issn      = "1613-0073",
  publisher = "CEUR Workshop Proceedings",
}

This paper presents Linked Data Reactor (LD-Reactor or LD-R) as a framework for developing exible and reusable User Interface components for Linked Data applications. LD-Reactor utilizes Facebook's ReactJS components, Flux architecture and Yahoo's Fluxible framework for isomorphic Web applications. It also exploits Semantic-UI framework for exible UI themes. LD-R aims to apply the idea of component-based application development into RDF data model hence enhancing current user interfaces to view, browse and edit Linked Data.

@misc{9d1847e6571e4a07a23d8b2857aaed65,
  title  = "LOTUS: Adaptive Search for Big Linked Data",
  author = "F. Ilievski and W.G.J. Beek and {van Erp}, M.G.J. and L.J. Rietveld and K.S. Schlobach",
  year   = "2016",
}

@inbook{f61941f98374409c990fb5f25443b77d,
  title     = "LOTUS: Adaptive text search for big linked data",
  abstract  = "Finding relevant resources on the Semantic Web today is a dirty job: no centralized query service exists and the support for natural language access is limited. We present LOTUS: Linked Open Text Un- leaShed, a text-based entry point to a massive subset of today’s Linked Open Data Cloud. Recognizing the use case dependency of resource re- trieval, LOTUS provides an adaptive framework in which a set of match- ing and ranking algorithms are made available. Researchers and develop- ers are able to tune their own LOTUS index by choosing and combining the matching and ranking algorithms that suit their use case best. In this paper, we explain the LOTUS approach, its implementation and the functionality it provides. We demonstrate the ease with which LOTUS enables text-based resource retrieval at an unprecedented scale in con- crete and domain-specific scenarios. Finally, we provide evidence for the scalability of LOTUS with respect to the LOD Laundromat, the largest collection of easily accessible Linked Open Data currently available.",
  keywords  = "Findability, Scalable data management, Semantic search, Text indexing",
  author    = "F. Ilievski and Wouter Beek and {van Erp}, Marieke and Laurens Rietveld and Stefan Schlobach",
  year      = "2016",
  doi       = "10.1007/978-3-319-34129-3_29",
  isbn      = "9783319341286",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "470--485",
  booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}

Finding relevant resources on the Semantic Web today is a dirty job: no centralized query service exists and the support for natural language access is limited. We present LOTUS: Linked Open Text Un- leaShed, a text-based entry point to a massive subset of today’s Linked Open Data Cloud. Recognizing the use case dependency of resource re- trieval, LOTUS provides an adaptive framework in which a set of match- ing and ranking algorithms are made available. Researchers and develop- ers are able to tune their own LOTUS index by choosing and combining the matching and ranking algorithms that suit their use case best. In this paper, we explain the LOTUS approach, its implementation and the functionality it provides. We demonstrate the ease with which LOTUS enables text-based resource retrieval at an unprecedented scale in con- crete and domain-specific scenarios. Finally, we provide evidence for the scalability of LOTUS with respect to the LOD Laundromat, the largest collection of easily accessible Linked Open Data currently available.

@article{fafe4ddf3ff14b408a8d400a4331a3f0,
  title     = "Managing metadata for science, technology and innovation studies: The RISIS case",
  abstract  = "Here, we describe the RISIS-SMS metadata system, developed to support the use of heterogeneous datasets in the field of Science, Technology and Innovation Studies (STIS). These data are partly within the RISIS infrastructure, but often elsewhere. The system has three aims: (i) to help researchers to search for and understand data that will help to answer specific research questions, without having to access or download the data. As datasets often have restricted access, browsing metadata is a key feature of the system: researchers need help identifying the relevant data from different sources for their research, and for which data it is worthwhile asking for access; (ii) to support the enrichment of dataBy linking the metadata system to the Linked Open Data environment (LOD); (iii) to facilitate application-driven data integration.",
  keywords  = "metadata, linked data, research infrastructures, digital humanities, science & technologies studies",
  author    = "Idrissou, {Al Koudous} and Ali Khalili and Rinke Hoekstra and Besselaar, {Peter Van den}",
  year      = "2016",
  volume    = "1608",
  pages     = "15--20",
  journal   = "CEUR workshop proceedings",
  issn      = "1613-0073",
  publisher = "CEUR Workshop Proceedings",
}

Here, we describe the RISIS-SMS metadata system, developed to support the use of heterogeneous datasets in the field of Science, Technology and Innovation Studies (STIS). These data are partly within the RISIS infrastructure, but often elsewhere. The system has three aims: (i) to help researchers to search for and understand data that will help to answer specific research questions, without having to access or download the data. As datasets often have restricted access, browsing metadata is a key feature of the system: researchers need help identifying the relevant data from different sources for their research, and for which data it is worthwhile asking for access; (ii) to support the enrichment of dataBy linking the metadata system to the Linked Open Data environment (LOD); (iii) to facilitate application-driven data integration.

@article{9a629914bb37409db65a1698e87e8040,
  title     = "Phil@Scale: Computational methods within philosophy",
  abstract  = "In this paper we report the results of Phil@Scale, a project directed at the development of computational methods for (the history of) philosophy.1 In this project, philosophers and computer scientists together created SalVe, a tool that helps philosophers answering text-based questions. SalVe has been tested successfully on the Wissenschaftslehre (1837), an extensive work by the Bohemian polymath Bernard Bolzano (1781-1848). Bolzano was a philosopher, mathematician and theologian whose work has been of fundamental importance for the development of Western logic and the foundation of sciences such as mathematics and computer science. The testing of SalVe on the Wissenschaftslehre reveals that with respect to certain questions within philosophy valuable contributions are obtained by applying even rather simple, well-known computational techniques. We conclude that there is definitely a future for computational methods within text-based philosophical research. We explain how SalVe can be used within philosophical research that relies on textual sources. We will start out with an explanation of our aims in developing SalVe and give a short description of SalVe's functionalities, followed by a technical description of the tool. Then we will give a concrete example of how SalVe aids philosophical research. We conclude the paper with an evaluation of the potential of Digital Humanities tools for philosophy, and the challenges that face us if we wish to continue this development further.",
  author    = "{Van Wierst}, Pauline and Sanne Vrijenhoek and Stefan Schlobach and Arianna Betti",
  year      = "2016",
  volume    = "1681",
  journal   = "CEUR workshop proceedings",
  issn      = "1613-0073",
  publisher = "CEUR Workshop Proceedings",
}

In this paper we report the results of Phil@Scale, a project directed at the development of computational methods for (the history of) philosophy.1 In this project, philosophers and computer scientists together created SalVe, a tool that helps philosophers answering text-based questions. SalVe has been tested successfully on the Wissenschaftslehre (1837), an extensive work by the Bohemian polymath Bernard Bolzano (1781-1848). Bolzano was a philosopher, mathematician and theologian whose work has been of fundamental importance for the development of Western logic and the foundation of sciences such as mathematics and computer science. The testing of SalVe on the Wissenschaftslehre reveals that with respect to certain questions within philosophy valuable contributions are obtained by applying even rather simple, well-known computational techniques. We conclude that there is definitely a future for computational methods within text-based philosophical research. We explain how SalVe can be used within philosophical research that relies on textual sources. We will start out with an explanation of our aims in developing SalVe and give a short description of SalVe's functionalities, followed by a technical description of the tool. Then we will give a concrete example of how SalVe aids philosophical research. We conclude the paper with an evaluation of the potential of Digital Humanities tools for philosophy, and the challenges that face us if we wish to continue this development further.

@misc{0bb6ee06d05a48a2b0000ac820655834,
  title    = "Publishing and Consuming Linked Data",
  abstract = "13897",
  author   = "L.J. Rietveld",
  note     = "Exacte Wetenschappen Naam instelling promotie: Vrije Universiteit Amsterdam Naam instelling onderzoek: Vrije Universiteit Amsterdam",
  year     = "2016",
  school   = "Vrije Universiteit Amsterdam",
}

13897

@misc{c61af5870a364375bc61f835f38c24de,
  title    = "Refining Statistical Data on the Web",
  abstract = "13901",
  author   = "{Merono Penuela}, Albert",
  note     = "Exacte Wetenschappen Naam instelling promotie: Vrije Universiteit Amsterdam Naam instelling onderzoek: Vrije Universiteit Amsterdam",
  year     = "2016",
  school   = "Vrije Universiteit Amsterdam",
}

13901

@book{bfd5feefda6646ad8bb867d8da1b4691,
  title     = "RISIS Integrated platform, Architecture and roadmap.: RISIS Deliverable 25.2",
  author    = "Guillaume Orsal and Philippe Bruecker and Marc Barbier and {van den Besselaar}, P.A.A. and A. Khalili",
  year      = "2016",
  publisher = "Universite Paris Est - IFRIS",
}

@article{62ad2a6cd97a4be38502d5f84e54ec29,
  title     = "SCRY: Extending SPARQL with custom data processing methods for the life sciences",
  keywords  = "customization, data processing, rdf generation, SPARQL, extension",
  author    = "Bas Stringer and Albert Meroño-peñuela and Sanne Abeln and {van Harmelen}, Frank and Jaap Heringa",
  year      = "2016",
  volume    = "1795",
  pages     = "1--10",
  journal   = "CEUR workshop proceedings",
  issn      = "1613-0073",
  publisher = "CEUR Workshop Proceedings",
}

@article{48ce34ae6f064ad08f20a48bdfaaea18,
  title     = "Selected papers from the combined EKAW 2014 and Semantic Web journal track",
  author    = "Stefan Schlobach and Krzysztof Janowicz",
  year      = "2016",
  doi       = "10.3233/SW-160229",
  volume    = "7",
  pages     = "333--334",
  journal   = "Semantic Web",
  issn      = "1570-0844",
  publisher = "IOS Press",
  number    = "4",
}

@inbook{6b8910eb91d746ed84e869023557eb2a,
  title     = "SMS: a linked open data infrastructure for science and innovation studies.",
  author    = "{van den Besselaar}, Peter and Ali Khalili and Oladele Idrissou and K.S. Schlobach and {van Harmelen}, F.A.H.",
  year      = "2016",
  pages     = "106--114",
  editor    = "Ismael Rafols",
  booktitle = "Peripheries, Frontiers and Beyond; proceedings of the 21st STI Conference",
  publisher = "University Valencia",
}

@article{29b76753dd464f068489de2701391d51,
  title     = "SWISH: An integrated semantic web notebook",
  author    = "Wouter Beek and Jan Wielemaker",
  year      = "2016",
  volume    = "1690",
  journal   = "CEUR workshop proceedings",
  issn      = "1613-0073",
  publisher = "CEUR Workshop Proceedings",
}

@inbook{888950bfc54943609de37cd94c2c9063,
  title     = "SWISH: An Integrated Semantic Web Notebook",
  author    = "Wouter Beek and Jan Wielemaker",
  year      = "2016",
  booktitle = "Proceedings of the ISWC 2016 Posters Demonstrations Track co-located with 15th International Semantic Web Conference (ISWC 2016), Kobe, Japan, October 19, 2016.",
}

@article{aeeb6a77c22d4ecebcef47cedec27955,
  title     = "SWISH for prototyping clinical guideline interactions theory",
  abstract  = "SWISH provides a general purpose collaborative infrastructure for applying Prolog reasoning over an RDF dataset together with features that facilitates prototyping Semantic Web applications. In this paper we report on the use of SWISH for efficiently developing a prototype for detection of clinical guideline interactions. These guidelines are a set of medical recommendations meant for supporting doctors on tackling a single disease. However, often guidelines need to be combined for treating patients that suffer from multiple diseases, and then a number of interactions can occur. The generic interaction rules are implemented in SWI-Prolog and the guideline RDF-data is enriched with clinical Linked Open Data (LOD) (e.g. Drugbank, Sider). We show the implementation of the proposed theory about interaction detection in a case-study on combining three guidelines. The experiment is interactively described using a SWISH notebook and the results are graphical visualised empowered by graphviz.",
  keywords  = "Clinical guideline interactions, Multimorbidity, Prolog, RDF, SWISH",
  author    = "{Carretta Zamborlini}, Veruska and Jan Wielemaker and {Da Silveira}, Marcos and Cedric Pruski and {ten Teije}, Annette and {van Harmelen}, F.A.H.",
  year      = "2016",
  volume    = "1795",
  journal   = "CEUR workshop proceedings",
  issn      = "1613-0073",
  publisher = "CEUR Workshop Proceedings",
}

SWISH provides a general purpose collaborative infrastructure for applying Prolog reasoning over an RDF dataset together with features that facilitates prototyping Semantic Web applications. In this paper we report on the use of SWISH for efficiently developing a prototype for detection of clinical guideline interactions. These guidelines are a set of medical recommendations meant for supporting doctors on tackling a single disease. However, often guidelines need to be combined for treating patients that suffer from multiple diseases, and then a number of interactions can occur. The generic interaction rules are implemented in SWI-Prolog and the guideline RDF-data is enriched with clinical Linked Open Data (LOD) (e.g. Drugbank, Sider). We show the implementation of the proposed theory about interaction detection in a case-study on combining three guidelines. The experiment is interactively described using a SWISH notebook and the results are graphical visualised empowered by graphviz.

@inbook{4f2fea23a5e849ff801af292650d9fdf,
  title     = "The semantic web in an SMS",
  abstract  = "Many ICT applications and services, including those from the Semantic Web, rely on the Web for the exchange of data. This includes expensive server and network infrastructures. Most rural areas of developing countries are not reached by the Web and its possibilities, while at the same time the ability to share knowledge has been identified as a key enabler for development. To make widespread knowledge sharing possible in these rural areas, the notion of the Web has to be downscaled based on the specific low-resource infrastructure in place. In this paper, we introduce SPARQL over SMS, a solution for Web-like exchange of RDF data over cellular networks in which HTTP is substituted by SMS. We motivate and validate this through two use cases in West Africa. We present the design and implementation of the solution, along with a data compression method that combines generic compression strategies and strategies that use Semantic Web specific features to reduce the size of RDF before it is transferred over the low-bandwidth cellular network.",
  author    = "Onno Valkering and {de Boer}, Victor and Gossa Lô and Romy Blankendaal and Stefan Schlobach",
  year      = "2016",
  doi       = "10.1007/978-3-319-49004-5_45",
  isbn      = "9783319490038",
  volume    = "10024 LNAI",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "697--712",
  booktitle = "Knowledge Engineering and Knowledge Management - 20th International Conference, EKAW 2016, Proceedings",
}

Many ICT applications and services, including those from the Semantic Web, rely on the Web for the exchange of data. This includes expensive server and network infrastructures. Most rural areas of developing countries are not reached by the Web and its possibilities, while at the same time the ability to share knowledge has been identified as a key enabler for development. To make widespread knowledge sharing possible in these rural areas, the notion of the Web has to be downscaled based on the specific low-resource infrastructure in place. In this paper, we introduce SPARQL over SMS, a solution for Web-like exchange of RDF data over cellular networks in which HTTP is substituted by SMS. We motivate and validate this through two use cases in West Africa. We present the design and implementation of the solution, along with a data compression method that combines generic compression strategies and strategies that use Semantic Web specific features to reduce the size of RDF before it is transferred over the low-bandwidth cellular network.

@inbook{fa13d88f82b7462283a1cc9b8b232f44,
  title     = "The song remains the same: Lossless conversion and streaming of MIDI to RDF and back",
  abstract  = "In this demo, we explore the potential of RDF as a representation format for digital music. Digital music is broadly used today in many professional music production environments. For decades, MIDI (Musical Instrument Digital Interface) has been the standard for digital music exchange between musicians and devices, albeit not in a Web friendly way. We show the potential of expressing digital music as Linked Data, using our midi2rdf suite of tools to convert and stream digital music in MIDI format to RDF. The conversion allows for lossless round tripping: we can reconstruct a MIDI file identical to the original using its RDF representation. The streaming uses an existing, novel generative audio matching algorithm that we use to broadcast, with very low latency, RDF triples of MIDI events coming from arbitrary analog instruments.",
  keywords  = "Linked data, MIDI, Music streams, RDF",
  author    = "Albert Meroño-Peñuela and Rinke Hoekstra",
  year      = "2016",
  doi       = "10.1007/978-3-319-47602-5_38",
  isbn      = "9783319476018",
  volume    = "9989 LNCS",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "194--199",
  booktitle = "The Semantic Web - ESWC 2016 Satellite Events, Revised Selected Papers",
}

In this demo, we explore the potential of RDF as a representation format for digital music. Digital music is broadly used today in many professional music production environments. For decades, MIDI (Musical Instrument Digital Interface) has been the standard for digital music exchange between musicians and devices, albeit not in a Web friendly way. We show the potential of expressing digital music as Linked Data, using our midi2rdf suite of tools to convert and stream digital music in MIDI format to RDF. The conversion allows for lossless round tripping: we can reconstruct a MIDI file identical to the original using its RDF representation. The streaming uses an existing, novel generative audio matching algorithm that we use to broadcast, with very low latency, RDF triples of MIDI events coming from arbitrary analog instruments.

@misc{7559caac92f74c2381a30ea519bbe559,
  title  = "Towards an open data infrastructure for STI data. OECD Blue Sky Conference, Gent, September 2016",
  author = "{van den Besselaar}, Peter and Ali Khalili and {de Graaf}, {Klaas Andries} and Oladele Idrissou and A. Loizou and K.S. Schlobach and {van Harmelen}, F.A.H.",
  year   = "2016",
}

@article{fd1c0df2db4246e19aabe172262616ca,
  title   = "大数据时代的语义技术",
  author  = "Zhisheng Huang",
  year    = "2016",
  volume  = "2016",
  journal = "Journal of Digital Library Forum",
  issn    = "1673-2286",
  number  = "10",
}

@article{e8f8022d7e21492db221d89474cd72a4,
  title     = "The Aggregate Dutch Historical Censuses",
  abstract  = "Historical censuses have an enormous potential for research. In order to fully use this potential, harmonization of these censuses is essential. During the last decades, enormous efforts have been undertaken in digitizing the published aggregated outcomes of the Dutch historical censuses (1795-1971). Although the accessibility has been improved enormously, researchers must cope with hundreds of heterogeneous and disconnected Excel tables. As a result, the census is still for the most part an untapped source of information. The authors describe the main harmonization challenges of the census and how they work toward one harmonized dataset. They propose a specific approach and model in creating an interlinked census dataset in the Semantic Web using the Resource Description Framework technology.",
  keywords  = "harmonization, historical censuses, historical demography, RDF, Semantic Web, social and economic history",
  author    = "Ashkan Ashkpour and Albert Meroño-Peñuela and Kees Mandemakers",
  year      = "2015",
  month     = "10",
  doi       = "10.1080/01615440.2015.1026009",
  volume    = "48",
  pages     = "230--245",
  journal   = "Historical Methods",
  issn      = "0161-5440",
  publisher = "Routledge",
  number    = "4",
}

Historical censuses have an enormous potential for research. In order to fully use this potential, harmonization of these censuses is essential. During the last decades, enormous efforts have been undertaken in digitizing the published aggregated outcomes of the Dutch historical censuses (1795-1971). Although the accessibility has been improved enormously, researchers must cope with hundreds of heterogeneous and disconnected Excel tables. As a result, the census is still for the most part an untapped source of information. The authors describe the main harmonization challenges of the census and how they work toward one harmonized dataset. They propose a specific approach and model in creating an interlinked census dataset in the Semantic Web using the Resource Description Framework technology.

@inbook{c469e91b8f534261bde83b6a2a70a732,
  title     = "Graphalytics: A big data benchmark for graph-processing platforms",
  abstract  = "Graphs are increasingly used in industry, governance, and science. This has stimulated the appearance of many and diverse graph-processing platforms. Although platform di- versity is beneficial, it also makes it very challenging to select the best platform for an application domain or one of its im- portant applications, and to design new and tune existing platforms. Continuing a long tradition of using benchmark- ing to address such challenges, in this work we present our vision for Graphalytics, a big data benchmark for graph- processing platforms. We have already benchmarked with Graphalytics a variety of popular platforms, such as Giraph, GraphX, and Neo4j.",
  author    = "Mihai Capota and Tim Hegeman and Alexandru Iosup and Arnau Prat-Pérez and Orri Erling and Peter Boncz",
  year      = "2015",
  month     = "5",
  doi       = "10.1145/2764947.2764954",
  booktitle = "3rd International Workshop on Graph Data Management Experiences and Systems, GRADES 2015 - co-located with SIGMOD/PODS 2015",
  publisher = "Association for Computing Machinery, Inc",
}

Graphs are increasingly used in industry, governance, and science. This has stimulated the appearance of many and diverse graph-processing platforms. Although platform di- versity is beneficial, it also makes it very challenging to select the best platform for an application domain or one of its im- portant applications, and to design new and tune existing platforms. Continuing a long tradition of using benchmark- ing to address such challenges, in this work we present our vision for Graphalytics, a big data benchmark for graph- processing platforms. We have already benchmarked with Graphalytics a variety of popular platforms, such as Giraph, GraphX, and Neo4j.

@inbook{3d0fe75c8f744dfa901d715dbb8ad283,
  title     = "The LDBC social network benchmark: Interactive workload",
  abstract  = "The Linked Data Benchmark Council (LDBC) is now two years underway and has gathered strong industrial participation for its mission to establish benchmarks, and benchmarking practices for evaluating graph data management systems. The LDBC introduced a new choke-point driven methodology for developing benchmark workloads, which combines user input with input from expert systems architects, which we outline. This paper describes the LDBC Social Network Benchmark (SNB), and presents database benchmarking innovation in terms of graph query functionality tested, correlated graph generation techniques, as well as a scalable benchmark driver on a workload with complex graph dependencies. SNB has three query workloads under development: Interactive, Business Intelligence, and Graph Algorithms. We describe the SNB Interactive Workload in detail and illustrate the workload with some early results, as well as the goals for the two other workloads.",
  author    = "Orri Erling and Alex Averbuch and Josep-Lluis Larriba-Pey and Hassan Chafi and Andrey Gubichev and Arnau Prat and Pham, {Minh Duc} and Peter Boncz",
  year      = "2015",
  month     = "5",
  doi       = "10.1145/2723372.2742786",
  volume    = "2015-May",
  pages     = "619--630",
  booktitle = "SIGMOD 2015 - Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data",
  publisher = "Association for Computing Machinery (ACM)",
}

The Linked Data Benchmark Council (LDBC) is now two years underway and has gathered strong industrial participation for its mission to establish benchmarks, and benchmarking practices for evaluating graph data management systems. The LDBC introduced a new choke-point driven methodology for developing benchmark workloads, which combines user input with input from expert systems architects, which we outline. This paper describes the LDBC Social Network Benchmark (SNB), and presents database benchmarking innovation in terms of graph query functionality tested, correlated graph generation techniques, as well as a scalable benchmark driver on a workload with complex graph dependencies. SNB has three query workloads under development: Interactive, Business Intelligence, and Graph Algorithms. We describe the SNB Interactive Workload in detail and illustrate the workload with some early results, as well as the goals for the two other workloads.

@article{3711c0eae7cb45b9bec21e26f594fb4b,
  title     = "Why the Data Train Needs Semantic Rails",
  abstract  = "While catchphrases such as big data, smart data, data-intensive science, or smart dust highlight different aspects, they share a common theme - namely, a shift toward a data-centered perspective in which the synthesis and analysis of data at an ever-increasing spatial, temporal, and thematic resolution promise new insights, while, at the same time, reduce the need for strong domain theories as starting points. In terms of the envisioned methodologies, those catchphrases tend to emphasize the role of predictive analytics, that is, statistical techniques including data mining and machine learning, as well as supercomputing. Interestingly, however, while this perspective takes the availability of data as a given, it does not answer the question how one would discover the required data in today's chaotic information universe, how one would understand which data sets can be meaningfully integrated, and how to communicate the results to humans and machines alike. The semantic web addresses these questions. In the following, we argue why the data train needs semantic rails. We point out that making sense of data and gaining new insights work best if inductive and deductive techniques go hand-in-hand instead of competing over the prerogative of interpretation.",
  author    = "Krzysztof Janowicz and Pascal Hitzler and Hendler, {James A.} and {van Harmelen}, Frank",
  year      = "2015",
  month     = "3",
  volume    = "36",
  pages     = "5--14",
  journal   = "The AI Magazine",
  issn      = "0738-4602",
  publisher = "AI Access Foundation",
  number    = "1",
}

While catchphrases such as big data, smart data, data-intensive science, or smart dust highlight different aspects, they share a common theme - namely, a shift toward a data-centered perspective in which the synthesis and analysis of data at an ever-increasing spatial, temporal, and thematic resolution promise new insights, while, at the same time, reduce the need for strong domain theories as starting points. In terms of the envisioned methodologies, those catchphrases tend to emphasize the role of predictive analytics, that is, statistical techniques including data mining and machine learning, as well as supercomputing. Interestingly, however, while this perspective takes the availability of data as a given, it does not answer the question how one would discover the required data in today's chaotic information universe, how one would understand which data sets can be meaningfully integrated, and how to communicate the results to humans and machines alike. The semantic web addresses these questions. In the following, we argue why the data train needs semantic rails. We point out that making sense of data and gaining new insights work best if inductive and deductive techniques go hand-in-hand instead of competing over the prerogative of interpretation.

@article{f968edbb167c4315a9c9922f6017f3b6,
  title   = "Tracking down the habitat of folk songs",
  author  = "B. Janssen and A. Merono and A. Ashkpour and C.D.M. Gueret",
  year    = "2015",
  month   = "1",
  journal = "eHumanities Magazine 4",
}

@article{7ccc5e56b3e5482ba6566d77dd0cf715,
  title   = "We've Always Been Digital",
  author  = "{Merono Penuela}, Albert",
  year    = "2015",
  month   = "1",
  journal = "eHumanities Magazine 4",
}

@inbook{85421a7bb476400da0040eb31b196374,
  title     = "A Compact In-Memory Dictionary for RDF data",
  abstract  = "While almost all dictionary compression techniques focus on static RDF data, we present a compact in-memory RDF dictionary for dynamic and streaming data. To do so, we analysed the structure of terms in real-world datasets and observed a high degree of common prefixes. We studied the applicability of Trie data structures on RDF data to reduce the memory occupied by common prefixes and discovered that all existing Trie implementations lead to either poor performance, or an excessive memory wastage. In our approach, we address the existing limitations of Tries for RDF data, and propose a new variant of Trie which contains some optimizations explicitly designed to improve the performance on RDF data. Furthermore, we show how we use this Trie as an in-memory dictionary by using as numerical ID a memory address instead of an integer counter. This design removes the need for an additional decoding data structure, and further reduces the occupied memory. An empirical analysis on realworld datasets shows that with a reasonable overhead our technique uses 50–59% less memory than a conventional uncompressed dictionary.",
  author    = "Bazoubandi, {Hamid R.} and {de Rooij}, Steven and Jacopo Urbani and {ten Teije}, Annette and {van Harmelen}, Frank and Henri Bal",
  note      = "Proceedings title: Proceedings of the twelfth European Semantic Web Conference Publisher: Springer Place of publication: Berlin",
  year      = "2015",
  doi       = "10.1007/978-3-319-18818-8_13",
  volume    = "9088",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "205--220",
  booktitle = "The Semantic Web: Latest Advances and New Domains - 12th European Semantic Web Conference, ESWC 2015, Proceedings",
}

While almost all dictionary compression techniques focus on static RDF data, we present a compact in-memory RDF dictionary for dynamic and streaming data. To do so, we analysed the structure of terms in real-world datasets and observed a high degree of common prefixes. We studied the applicability of Trie data structures on RDF data to reduce the memory occupied by common prefixes and discovered that all existing Trie implementations lead to either poor performance, or an excessive memory wastage. In our approach, we address the existing limitations of Tries for RDF data, and propose a new variant of Trie which contains some optimizations explicitly designed to improve the performance on RDF data. Furthermore, we show how we use this Trie as an in-memory dictionary by using as numerical ID a memory address instead of an integer counter. This design removes the need for an additional decoding data structure, and further reduces the occupied memory. An empirical analysis on realworld datasets shows that with a reasonable overhead our technique uses 50–59% less memory than a conventional uncompressed dictionary.

@inbook{efec5bf19fa345ff8264ce823017de8c,
  title     = "Analyzing Recommendations Interactions in Clinical Guidelines: Impact of action type hierarchies and causation beliefs",
  abstract  = "Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations meant to address each condition. Although some approaches have been proposed to address this issue, important features still require more investigation, such as (re)usability and scalability. To this end, this paper presents an approach that relies on reusable rules for detecting interactions among recommendations coming from various guidelines. It extends previously proposed models by introducing the notions of action type hierarchy and causation beliefs, and provides a systematic analysis of relevant interactions in the context of multimorbidity. Finally, the approach is assessed based on a case-study taken from the literature to highlight the added value of the approach.",
  keywords  = "Clinical knowledge representation, Combining medical guidelines, Multimorbidity",
  author    = "{Carretta Zamborlini}, Veruska and {Da Silveira}, Marcos and Cedric Pruski and {ten Teije}, Annette and {van Harmelen}, Frank",
  year      = "2015",
  doi       = "10.1007/978-3-319-19551-3_40",
  isbn      = "9783319195506",
  volume    = "9105",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "317--326",
  booktitle = "Artificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings",
}

Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations meant to address each condition. Although some approaches have been proposed to address this issue, important features still require more investigation, such as (re)usability and scalability. To this end, this paper presents an approach that relies on reusable rules for detecting interactions among recommendations coming from various guidelines. It extends previously proposed models by introducing the notions of action type hierarchy and causation beliefs, and provides a systematic analysis of relevant interactions in the context of multimorbidity. Finally, the approach is assessed based on a case-study taken from the literature to highlight the added value of the approach.

@inbook{f9ec22a41c9d463d8226fb6a652fa451,
  title     = "An Intelligent Monitoring System for the Safety of Building Structure under the W2T Framework",
  abstract  = "Monitoring systems for the safety of building structure (SBS) can provide people with important data related to main supporting points in a building and then help people to make a reasonable maintenance schedule. However, more and more data bring a challenge for data management and data mining. In order to meet this challenge, under the framework of Wisdom Web of Things (W2T), we design a monitoring system for the SBS by using the semantic and the multisource data fusion technologies. This system establishes a dynamical data cycle among the physical world (buildings), the social world (humans), and the cyber world (computers) and provides various services in the monitoring process to alleviate engineers' workload. Furthermore, all data in the cyber world are organized as the raw data, the semantic information, and the multisource knowledge. Based on this organization, we can concentrate on the data fusion from the viewpoints of time, space, and multisensor. At last, a prototype system powered by the semantic platform LarKC is tested from the aspects of sample performance and time consumption. In particular, noisy data (i.e., inconsistent, abnormal, or error data) are detected through the fusion of multisource knowledge, and some rule-based reasoning is conducted to provide personalized service.",
  author    = "H. Wang and Z. Huang and N. Zhong and Y. Han and F. Zhang",
  year      = "2015",
  doi       = "10.1155/2015/378694",
  booktitle = "Journal of Distributed Sensor Networks",
}

Monitoring systems for the safety of building structure (SBS) can provide people with important data related to main supporting points in a building and then help people to make a reasonable maintenance schedule. However, more and more data bring a challenge for data management and data mining. In order to meet this challenge, under the framework of Wisdom Web of Things (W2T), we design a monitoring system for the SBS by using the semantic and the multisource data fusion technologies. This system establishes a dynamical data cycle among the physical world (buildings), the social world (humans), and the cyber world (computers) and provides various services in the monitoring process to alleviate engineers' workload. Furthermore, all data in the cyber world are organized as the raw data, the semantic information, and the multisource knowledge. Based on this organization, we can concentrate on the data fusion from the viewpoints of time, space, and multisensor. At last, a prototype system powered by the semantic platform LarKC is tested from the aspects of sample performance and time consumption. In particular, noisy data (i.e., inconsistent, abnormal, or error data) are detected through the fusion of multisource knowledge, and some rule-based reasoning is conducted to provide personalized service.

@book{dfa31ef6a2fd4867a6b27654aabff10c,
  title     = "ARIADNE: First Report on Data Mining",
  abstract  = "Recent years have witnessed a growing interest from archaeological communities in Linked Data. ARIADNE, the AdvancedResearch Infrastructure for Archaeological Data set Networking in Europe, facilitates a central web portal that providesaccess to archaeological data from various sources. Parts of these data have been being published as Linked Data, andare currently available in the Linked Open Data cloud. With it, the nature of these data has shifted from unstructuredto structured. This presents new opportunities for data mining. In this work, we investigate to what extend data mining can contribute to the understanding of linked archaeological data, and which form would best meet the communities' needs.",
  author    = "W.X. Wilcke and {de Boer}, Viktor and {van Harmelen}, F.A.H. and {de Kleijn}, Mauritius and M. Wansleeben",
  year      = "2015",
  series    = "Ariadne",
  publisher = "Ariadne",
  number    = "D16.1",
}

Recent years have witnessed a growing interest from archaeological communities in Linked Data. ARIADNE, the AdvancedResearch Infrastructure for Archaeological Data set Networking in Europe, facilitates a central web portal that providesaccess to archaeological data from various sources. Parts of these data have been being published as Linked Data, andare currently available in the Linked Open Data cloud. With it, the nature of these data has shifted from unstructuredto structured. This presents new opportunities for data mining. In this work, we investigate to what extend data mining can contribute to the understanding of linked archaeological data, and which form would best meet the communities' needs.

@inbook{c13a481d2b0045689c082bf384bd5015,
  title     = "A Semantic Smart Hospital Information System for Mental Disorders",
  author    = "Youjun Li and Z. Wan and J. Huang and J. Chen and Z. Huang and N. Zhong",
  year      = "2015",
  booktitle = "Proceedings of the 2015 IEEE/WIC/ACM International Conference on Web Intelligence (WI2015)",
}

@inbook{6c103170c3d44d4cb733f26940c0b629,
  title     = "Automatic Verification of Road Signs based on Ontology",
  author    = "D. Wang and Z. Huang and N. Zhong and D. Xu and X. Zhang and Zhi Wang",
  year      = "2015",
  series    = "4",
  pages     = "384--392",
  booktitle = "Journal of Wuhan University",
}

@inbook{a23086b321be43a5b2468b356ba2b7c5,
  title     = "Cognition-inspired route evaluation using mobile phone data",
  abstract  = "With the increasing popularity of mobile phones, large amounts of real and reliable mobile phone data are being generated every day. These mobile phone data represent the practical travel routes of users and imply the intelligence of them in selecting a suitable route. Usually, an experienced user knows which route is congested in a specified period of time but unblocked in another period of time. Moreover, a route used frequently and recently by a user is usually the suitable one to satisfy the user’s needs. Adaptive control of thought-rational (ACT-R) is a computational cognitive architecture, which provides a good framework to understand the principles and mechanisms of information organization, retrieval and selection in human memory. In this paper, we employ ACT-R to model the process of selecting a suitable route of users. We propose a cognition-inspired route evaluation method to mine the intelligence of users in selecting a suitable route, evaluate the suitability of the routes, and then recommend an ordered list of routes for subscribers. Experiments show that it is effective and feasible to evaluate the suitability of the routes inspired by cognition.",
  author    = "H. Wang and J. Huang and E. Zhou and Z. Huang and N. Zhong",
  year      = "2015",
  doi       = "10.1007/s11047-014-9479-9",
  pages     = "637--648",
  booktitle = "Journal of Natural Computing",
}

With the increasing popularity of mobile phones, large amounts of real and reliable mobile phone data are being generated every day. These mobile phone data represent the practical travel routes of users and imply the intelligence of them in selecting a suitable route. Usually, an experienced user knows which route is congested in a specified period of time but unblocked in another period of time. Moreover, a route used frequently and recently by a user is usually the suitable one to satisfy the user’s needs. Adaptive control of thought-rational (ACT-R) is a computational cognitive architecture, which provides a good framework to understand the principles and mechanisms of information organization, retrieval and selection in human memory. In this paper, we employ ACT-R to model the process of selecting a suitable route of users. We propose a cognition-inspired route evaluation method to mine the intelligence of users in selecting a suitable route, evaluate the suitability of the routes, and then recommend an ordered list of routes for subscribers. Experiments show that it is effective and feasible to evaluate the suitability of the routes inspired by cognition.

@article{7b4c0b8e14584116bb59fef9cce62f7f,
  title     = "CrowdLearn: Crowd-sourcing the Creation of Highly-structured e-Learning Content",
  author    = "D. Tarasowa and A. Khalili and S. Auer",
  note      = "PT: J; NR: 18; TC: 0; J9: INT J ENG PEDAGOG; PG: 8; GA: CZ3HA; UT: WOS:000366993800006",
  year      = "2015",
  doi       = "10.3991/ijep.v5i4.4951",
  volume    = "5",
  pages     = "47--54",
  journal   = "International Journal of Engineering Pedagogy",
  issn      = "2192-4880",
  publisher = "International Association of Online Engineering",
  number    = "4",
}

@book{50a1d221618d4a5f9f76280af1efa706,
  title     = "Depicting and selecting preferable approaches to knowledge clustering - RISIS deliverable",
  author    = "E Israel and L Villard and M Revollo and D Getz and V Segal and P Larédo and A. Khalili and A. Loizou and {van den Besselaar}, P.A.A. and V Veglio and B Lepori and E. Reale",
  year      = "2015",
  publisher = "SNI",
}

@inbook{1da843e38a53485383b9e72917ebd0b6,
  title     = "Detecting New Evidence for Evidence-based Guidelines Using a Semantic Distance Method",
  author    = "Q. Hu and Z. Huang and {ten Teije}, A.C.M. and {van Harmelen}, F.A.H.",
  year      = "2015",
  pages     = "307--316",
  booktitle = "Proceedings 15th Conference on Artificial Intelligence in Medicine",
  publisher = "Springer",
}

@article{aaf8c24aa8184513aff121db59682115,
  title     = "Enhancing reuse of structured eligibility criteria and supporting their relaxation.",
  abstract  = "Patient recruitment is one of the most important barriers to successful completion of clinical trials and thus to obtaining evidence about new methods for prevention, diagnostics and treatment. The reason is that recruitment is effort consuming. It requires the identification of candidate patients for the trial (the population under study), and verifying for each patient whether the eligibility criteria are met. The work we describe in this paper aims to support the comparison of population under study in different trials, and the design of eligibility criteria for new trials. We do this by introducing structured eligibility criteria, that enhance reuse of criteria across trials. We developed a method that allows for automated structuring of criteria from text. Additionally, structured eligibility criteria allow us to propose suggestions for relaxation of criteria to remove potentially unnecessarily restrictive conditions. We thereby increase the recruitment potential and generalizability of a trial.Our method for automated structuring of criteria enables us to identify related conditions and to compare their restrictiveness. The comparison is based on the general meaning of criteria, comprised of commonly occurring contextual patterns, medical concepts and constraining values. These are automatically identified using our pattern detection algorithm, state of the art ontology annotators and semantic taggers. The comparison uses predefined relations between the patterns, concept equivalences defined in medical ontologies, and threshold values. The result is a library of structured eligibility criteria which can be browsed using fine grained queries. Furthermore, we developed visualizations for the library that enable intuitive navigation of relations between trials, criteria and concepts. These visualizations expose interesting co-occurrences and correlations, potentially enhancing meta-research.The method for criteria structuring processes only certain types of criteria, which results in low recall of the method (18%) but a high precision for the relations we identify between the criteria (94%). Analysis of the approach from the medical perspective revealed that the approach can be beneficial for supporting trial design, though more research is needed.",
  author    = "K. Milian and Rinke Hoekstra and A. Bucur and {ten Teije}, A.C.M. and {van Harmelen}, F.A.H. and J. Paulissen",
  year      = "2015",
  doi       = "10.1016/j.jbi.2015.05.005",
  volume    = "56",
  pages     = "205--219",
  journal   = "Journal of Biomedical Informatics",
  issn      = "1532-0464",
  publisher = "Academic Press Inc.",
}

Patient recruitment is one of the most important barriers to successful completion of clinical trials and thus to obtaining evidence about new methods for prevention, diagnostics and treatment. The reason is that recruitment is effort consuming. It requires the identification of candidate patients for the trial (the population under study), and verifying for each patient whether the eligibility criteria are met. The work we describe in this paper aims to support the comparison of population under study in different trials, and the design of eligibility criteria for new trials. We do this by introducing structured eligibility criteria, that enhance reuse of criteria across trials. We developed a method that allows for automated structuring of criteria from text. Additionally, structured eligibility criteria allow us to propose suggestions for relaxation of criteria to remove potentially unnecessarily restrictive conditions. We thereby increase the recruitment potential and generalizability of a trial.Our method for automated structuring of criteria enables us to identify related conditions and to compare their restrictiveness. The comparison is based on the general meaning of criteria, comprised of commonly occurring contextual patterns, medical concepts and constraining values. These are automatically identified using our pattern detection algorithm, state of the art ontology annotators and semantic taggers. The comparison uses predefined relations between the patterns, concept equivalences defined in medical ontologies, and threshold values. The result is a library of structured eligibility criteria which can be browsed using fine grained queries. Furthermore, we developed visualizations for the library that enable intuitive navigation of relations between trials, criteria and concepts. These visualizations expose interesting co-occurrences and correlations, potentially enhancing meta-research.The method for criteria structuring processes only certain types of criteria, which results in low recall of the method (18%) but a high precision for the relations we identify between the criteria (94%). Analysis of the approach from the medical perspective revealed that the approach can be beneficial for supporting trial design, though more research is needed.

@inbook{6a9d7f0947f9432c9fa05c3a7d1cae74,
  title     = "Error analysis of Word Sense Disambiguation",
  author    = "R. Izquirdo and M.C. Postma and P.T.J.M. Vossen",
  year      = "2015",
  booktitle = "25th Meeting of Computational Linguistics in the Netherlands",
}

@inbook{dc50695aa6fb421080b44cf8bf27b079,
  title     = "Finding Evidence for Updates in Medical Guidelines",
  abstract  = "Medical guidelines are documents that describe optimal treatment for patients by medical practitioners based on current medical research (evidence), in the form of step-by-step recommendations. Because the field of medical research is very large and always evolving, keeping these guidelines up-to-date with the current state of the art is a difficult task. In this paper, we propose a method for finding relevant evidence for supporting the medical guideline updating process. Our method that takes from the evidence-based medical guideline the recommendations and their corresponding evidence as its input, and that queries PubMed, the world's largest search engine for medical citations, for potential new or improved evidence. We built a prototype and performed a feasibility study on a set of old recommendations, and compared the output to evidence for the newer version. The system succeeded in finding goal articles for 11 out of 16 recommendations, but in total, only 20 out of 71 articles were retrieved. Our ranking method for most relevant articles worked well for small result sets, but for large result sets it failed to rank the goal articles in the top 25 results.",
  keywords  = "Evidence-based medicine, Medical guideline updates, Medical guidelines",
  author    = "Roelof Reinders and {ten Teije}, Annette and Zisheng Huang",
  year      = "2015",
  pages     = "91--102",
  booktitle = "HEALTHINF 2015 - 8th International Conference on Health Informatics, Proceedings; Part of 8th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2015",
  publisher = "SciTePress",
}

Medical guidelines are documents that describe optimal treatment for patients by medical practitioners based on current medical research (evidence), in the form of step-by-step recommendations. Because the field of medical research is very large and always evolving, keeping these guidelines up-to-date with the current state of the art is a difficult task. In this paper, we propose a method for finding relevant evidence for supporting the medical guideline updating process. Our method that takes from the evidence-based medical guideline the recommendations and their corresponding evidence as its input, and that queries PubMed, the world's largest search engine for medical citations, for potential new or improved evidence. We built a prototype and performed a feasibility study on a set of old recommendations, and compared the output to evidence for the newer version. The system succeeded in finding goal articles for 11 out of 16 recommendations, but in total, only 20 out of 71 articles were retrieved. Our ranking method for most relevant articles worked well for small result sets, but for large result sets it failed to rank the goal articles in the top 25 results.

@article{4739c874bc7b4b8b80b515650dace968,
  title     = "foxPSL: A Fast, Optimized and eXtended PSL implementation",
  abstract  = "In this paper, we describe foxPSL, a fast, optimized and extended implementation of Probabilistic Soft Logic (PSL) based on the distributed graph processing framework Signal/Collect. PSL is one of the leading formalisms of statistical relational learning, a recently developed field of machine learning that aims at representing both uncertainty and rich relational structures, usually by combining logical representations with probabilistic graphical models. PSL can be seen as both a probabilistic logic and a template language for hinge-loss Markov Random Fields, a type of continuous Markov Random fields (MRF) in which Maximum a Posteriori inference is very efficient, since it can be formulated as a constrained convex minimization problem, as opposed to a discrete optimization problem for standard MRFs. From the logical perspective, a key feature of PSL is the capability to represent soft truth values, allowing the expression of complex domain knowledge, like degrees of truth, in parallel with uncertainty. foxPSL supports the full PSL pipeline from problem definition to a distributed solver that implements the Alternating Direction Method of Multipliers (ADMM) consensus optimization. It provides a Domain Specific Language that extends standard PSL with a class system and existential quantifiers, allowing for efficient grounding. Moreover, it implements a series of configurable optimizations, like optimized grounding of constraints and lazy inference, that improve grounding and inference time. We perform an extensive evaluation, comparing the performance of foxPSL to a state-of-the-art implementation of ADMM consensus optimization in GraphLab, and show an improvement in both inference time and solution quality. Moreover, we evaluate the impact of the optimizations on the execution time and discuss the trade-offs related to each optimization.",
  author    = "S. Magliacane and P. Stutz and P. Groth and A. Bernstein",
  year      = "2015",
  doi       = "10.1016/j.ijar.2015.05.012",
  journal   = "International Journal of Approximate Reasoning",
  issn      = "0888-613X",
  publisher = "Elsevier Inc.",
}

In this paper, we describe foxPSL, a fast, optimized and extended implementation of Probabilistic Soft Logic (PSL) based on the distributed graph processing framework Signal/Collect. PSL is one of the leading formalisms of statistical relational learning, a recently developed field of machine learning that aims at representing both uncertainty and rich relational structures, usually by combining logical representations with probabilistic graphical models. PSL can be seen as both a probabilistic logic and a template language for hinge-loss Markov Random Fields, a type of continuous Markov Random fields (MRF) in which Maximum a Posteriori inference is very efficient, since it can be formulated as a constrained convex minimization problem, as opposed to a discrete optimization problem for standard MRFs. From the logical perspective, a key feature of PSL is the capability to represent soft truth values, allowing the expression of complex domain knowledge, like degrees of truth, in parallel with uncertainty. foxPSL supports the full PSL pipeline from problem definition to a distributed solver that implements the Alternating Direction Method of Multipliers (ADMM) consensus optimization. It provides a Domain Specific Language that extends standard PSL with a class system and existential quantifiers, allowing for efficient grounding. Moreover, it implements a series of configurable optimizations, like optimized grounding of constraints and lazy inference, that improve grounding and inference time. We perform an extensive evaluation, comparing the performance of foxPSL to a state-of-the-art implementation of ADMM consensus optimization in GraphLab, and show an improvement in both inference time and solution quality. Moreover, we evaluate the impact of the optimizations on the execution time and discuss the trade-offs related to each optimization.

@inbook{ae668f92c8e24c3da114a3bae1694df5,
  title     = "FoxPSL: An Extended and Scalable PSL Implementation",
  author    = "S. Magliacane and P. Stutz and P. Groth and A. Bernstein",
  year      = "2015",
  booktitle = "AAAI Spring Symposium 2015 on Knowledge Representation and Reasoning",
}

@article{603d52e2383f4ea7a256bf976ffb4c31,
  title     = "Frank: The LOD cloud at your fingertips?",
  abstract  = "Large-scale, algorithmic access to LOD Cloud data has been hampered by the absence of queryable endpoints for many datasets, a plethora of serialization formats, and an abundance of idiosyncrasies such as syntax errors. As of late, very large-scale - hundreds of thousands of document, tens of billions of triples - access to RDF data has become possible thanks to the LOD Laundromat Web Service. In this paper we showcase Frank, a command-line interface to a very large collection of standards-compliant, real-world RDF data that can be used to run Semantic Web experiments and stress-test Linked Data applications.",
  author    = "Wouter Beek and Laurens Rietveld",
  year      = "2015",
  volume    = "1361",
  pages     = "41--46",
  journal   = "CEUR workshop proceedings",
  issn      = "1613-0073",
  publisher = "CEUR Workshop Proceedings",
}

Large-scale, algorithmic access to LOD Cloud data has been hampered by the absence of queryable endpoints for many datasets, a plethora of serialization formats, and an abundance of idiosyncrasies such as syntax errors. As of late, very large-scale - hundreds of thousands of document, tens of billions of triples - access to RDF data has become possible thanks to the LOD Laundromat Web Service. In this paper we showcase Frank, a command-line interface to a very large collection of standards-compliant, real-world RDF data that can be used to run Semantic Web experiments and stress-test Linked Data applications.

@inbook{a7ac3d57a0f6415187e64c0db66479c6,
  title     = "Frank: Algorithmic Access to the LOD Cloud",
  author    = "W.G.J. Beek and L.J. Rietveld",
  year      = "2015",
  pages     = "41--46",
  booktitle = "Proceedings of the Developer's Workshop ESWC 2015",
}

@misc{c0d3f8b79d574d49a286377515c14b7b,
  title    = "Frank: The LOD cloud at your fingertips?",
  abstract = "Large-scale, algorithmic access to LOD Cloud data has been hampered by the absence of queryable endpoints for many datasets, a plethora of serialization formats, and an abundance of idiosyncrasies such as syntax errors. As of late, very large-scale — hundreds of thousands of document, tens of billions of triples — access to RDF data has become possible thanks to the LOD Laundromat Web Service. In this paper we showcase Frank, a command-line interface to a very large collection of standards-compliant, real-world RDF data that can be used to run Semantic Web experiments and stress-test Linked Data applications.",
  author   = "Wouter Beek and Laurens Rietveld",
  year     = "2015",
}

Large-scale, algorithmic access to LOD Cloud data has been hampered by the absence of queryable endpoints for many datasets, a plethora of serialization formats, and an abundance of idiosyncrasies such as syntax errors. As of late, very large-scale — hundreds of thousands of document, tens of billions of triples — access to RDF data has become possible thanks to the LOD Laundromat Web Service. In this paper we showcase Frank, a command-line interface to a very large collection of standards-compliant, real-world RDF data that can be used to run Semantic Web experiments and stress-test Linked Data applications.

@inbook{44d79201f5274af0aa9b72d3ca9edf98,
  title     = "Hubble: Linked data Hub for clinical decision support",
  abstract  = "The AERS datasets is one of the few remaining, large publicly available medical data sets that until now have not been published as Linked Data. It is uniquely positioned amidst othermedical datasets. This paper describes the Hubble prototype system for clinical decision support that demonstrates the speed, ease and flexibility of producing and using a Linked Data version of the AERS dataset for clinical practice and research.",
  keywords  = "Adverse event, Clinical decision support, Health care, Linked data",
  author    = "Rinke Hoekstra and Sara Magliacane and Laurens Rietveld and {De Vries}, Gerben and Adianto Wibisono and Stefan Schlobach",
  year      = "2015",
  doi       = "10.1007/978-3-662-46641-4_45",
  isbn      = "9783662466407",
  volume    = "7540",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "458--462",
  booktitle = "The Semantic Web: ESWC 2012 Satellite Events - Revised Selected Papers",
}

The AERS datasets is one of the few remaining, large publicly available medical data sets that until now have not been published as Linked Data. It is uniquely positioned amidst othermedical datasets. This paper describes the Hubble prototype system for clinical decision support that demonstrates the speed, ease and flexibility of producing and using a Linked Data version of the AERS dataset for clinical practice and research.

@article{fddfc1d0460c4d7a99bb5e2cbb32156d,
  title     = "Identification of patients at risk for colorectal cancer in primary care: an explorative study with routine healthcare data",
  abstract  = "Background Early diagnosis of colorectal cancer (CRC) is likely to reduce burden of disease and improve treatment success. Estimation of the individual patient risk for CRC diagnostic determinants in a primary care setting has not been very successful as yet. The aim of our study is to improve prediction of CRC in patients selected for colonoscopy in the primary healthcare setting using readily available routine healthcare data. Patients and methods A cross-sectional study was carried out in the Julius General Practitioners' Network database. Patients referred for colonoscopy by their general practitioner (GP) between 2007 and 2012 were selected. We evaluated the association between long-term registered patient characteristics, symptoms and conditions, and colonoscopy test results with multivariable logistic regression. Results Two per cent (2787/140 000) of the patients between 30 and 85 years were found to be newly referred for colonoscopy by their GP, of whom 57 (2%) were diagnosed with CRC. Age 50 years or over, hypertension and the absence of preceding consultations for abdominal pain were independent predictors for CRC and/or high-risk adenomas, with an area under the curve of 0.65. Conclusion Three factors in routine care data combined might prove valuable in future strategies to improve the prediction of CRC risk in primary care. Improvement in quality and availability of routine care data for research and risk stratification is needed to optimize its usability for prediction purposes in daily practice. Impact Only referring patients at the highest risk for colonoscopy by the GP could decrease superfluous colonoscopies.",
  author    = "N.R Koning and L.N.G Moons and F.L. Büchner and C.W. Helsper and {ten Teije}, A.C.M. and M.E. Numans",
  year      = "2015",
  doi       = "10.1097/MEG.0000000000000472",
  volume    = "27",
  pages     = "1443--1448",
  journal   = "European Journal of Gastroenterology and Hepatology",
  issn      = "0954-691X",
  publisher = "Lippincott Williams and Wilkins",
  number    = "12",
}

Background Early diagnosis of colorectal cancer (CRC) is likely to reduce burden of disease and improve treatment success. Estimation of the individual patient risk for CRC diagnostic determinants in a primary care setting has not been very successful as yet. The aim of our study is to improve prediction of CRC in patients selected for colonoscopy in the primary healthcare setting using readily available routine healthcare data. Patients and methods A cross-sectional study was carried out in the Julius General Practitioners' Network database. Patients referred for colonoscopy by their general practitioner (GP) between 2007 and 2012 were selected. We evaluated the association between long-term registered patient characteristics, symptoms and conditions, and colonoscopy test results with multivariable logistic regression. Results Two per cent (2787/140 000) of the patients between 30 and 85 years were found to be newly referred for colonoscopy by their GP, of whom 57 (2%) were diagnosed with CRC. Age 50 years or over, hypertension and the absence of preceding consultations for abdominal pain were independent predictors for CRC and/or high-risk adenomas, with an area under the curve of 0.65. Conclusion Three factors in routine care data combined might prove valuable in future strategies to improve the prediction of CRC risk in primary care. Improvement in quality and availability of routine care data for research and risk stratification is needed to optimize its usability for prediction purposes in daily practice. Impact Only referring patients at the highest risk for colonoscopy by the GP could decrease superfluous colonoscopies.

@inbook{86f39ce706f04073a8fc2aec8926fed6,
  title     = "Identifying Evidence Quality for Updating Evidence-based Medical Guideline",
  author    = "Z. Huang and Q. Hu and {ten Teije}, A. and {van Harmelen}, F.",
  year      = "2015",
  booktitle = "Proceedings of International Joint WorkshopKR4HC 2015 - ProHealth 2015",
}

@inbook{cd7cd90ab9284d68ae98b8f54f65ca35,
  title     = "Identifying Evidence Quality for Updating Evidence-based Medical Guidelines",
  author    = "Z. Huang and Q. Hu and {ten Teije}, A.C.M. and {van Harmelen}, F.A.H.",
  year      = "2015",
  isbn      = "9783319265841",
  pages     = "51--64",
  editor    = "D Riano and R. Lenz and S. Miksch and M. Peleg and M. Reichert and {ten Teije}, A.C.M.",
  booktitle = "Knowledge Representation for Health Care, AIME 2015 International Joint Workshop, KR4HC/ProHealth 2015",
  publisher = "Springer",
}

@article{0cba7e1e14c04d4c8cf48f43f0aec6a2,
  title     = "Inferring Recommendation Interactions in Clinical Guidelines: Case-studies on Multimorbidity",
  author    = "V. Zamborlini and Rinke Hoekstra and {da Silveira}, M. and C. Pruski and {ten Teije}, A.C.M.",
  year      = "2015",
  journal   = "Semantic Web",
  issn      = "1570-0844",
  publisher = "IOS Press",
}

@book{a82caf00e1a143759c7f5543b6c84ea5,
  title     = "{Knowledge Representation for Health Care (AIME 2015 International Joint Workshop, KR4HC/ProHealth 2015)",
  author    = "D Riano and R. Lenz and S Miksch and M Peleg and M. Reichert and {ten Teije}, A.C.M.",
  year      = "2015",
  doi       = "10.1007/978-3-319-26585-8",
  isbn      = "9783319265841",
  series    = "LNAI",
  publisher = "Springer",
  number    = "9485",
}

@inbook{ef39b81fadc345f788ca499a78ccbb4c,
  title     = "Linked data-as-a-service: The semantic web redeployed",
  abstract  = "Ad-hoc querying is crucial to access information from Linked Data, yet publishing queryable RDF datasets on the Web is not a trivial exercise. The most compelling argument to support this claim is that the Web contains hundreds of thousands of data documents, while only 260 queryable SPARQL endpoints are provided. Even worse, the SPARQL endpoints we do have are often unstable, may not comply with the standards, and may differ in supported features. In other words, hosting data online is easy, but publishing Linked Data via a queryable API such as SPARQL appears to be too difficult. As a consequence, in practice, there is no single uniform way to query the LOD Cloud today. In this paper, we therefore combine a large-scale Linked Data publication project (LOD Laundromat) with a low-cost server-side interface (Triple Pattern Fragments), in order to bridge the gap between the Web of downloadable data documents and the Web of live queryable data. The result is a repeatable, low-cost, open-source data publication process. To demonstrate its applicability, we made over 650,000",
  keywords  = "API, Data publishing, Linked Data, Web Services",
  author    = "Laurens Rietveld and Ruben Verborgh and Wouter Beek and {Vander Sande}, Miel and Stefan Schlobach",
  year      = "2015",
  doi       = "10.1007/978-3-319-18818-8_29",
  isbn      = "9783319188171",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "471--487",
  booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}

Ad-hoc querying is crucial to access information from Linked Data, yet publishing queryable RDF datasets on the Web is not a trivial exercise. The most compelling argument to support this claim is that the Web contains hundreds of thousands of data documents, while only 260 queryable SPARQL endpoints are provided. Even worse, the SPARQL endpoints we do have are often unstable, may not comply with the standards, and may differ in supported features. In other words, hosting data online is easy, but publishing Linked Data via a queryable API such as SPARQL appears to be too difficult. As a consequence, in practice, there is no single uniform way to query the LOD Cloud today. In this paper, we therefore combine a large-scale Linked Data publication project (LOD Laundromat) with a low-cost server-side interface (Triple Pattern Fragments), in order to bridge the gap between the Web of downloadable data documents and the Web of live queryable data. The result is a repeatable, low-cost, open-source data publication process. To demonstrate its applicability, we made over 650,000

@inbook{5662be17f4b148779076a81e8a5e7e40,
  title     = "Linked Data-as-a-Service: The Semantic Web Redeployed",
  author    = "L.J. Rietveld and R. Verborgh and W.G.J. Beek and {Vander Sande}, M. and S. Schlobach",
  year      = "2015",
  pages     = "471--487",
  booktitle = "The Semantic Web. Latest Advances and New Domains",
  publisher = "Springer International Publishing",
}

@misc{1ecf8244410e4eb0bd41746e1c8cf4ff,
  title  = "Linked Data Journalism: Linking Data Sources to Journalistic Articles",
  author = "W.G.J. Beek",
  note   = "Proceedings title: The Future Of Journalism 2015: Risks, Threats and Opportunities",
  year   = "2015",
  pages  = "7--7",
}

@inbook{2cf3a78e7c914c72ae78bbb27c786359,
  title     = "LOD lab: Experiments at LOD scale",
  abstract  = "Contemporary Semantic Web research is in the business of optimizing algorithms for only a handful of datasets such as DBpedia, BSBM, DBLP and only a few more. This means that current practice does not generally take the true variety of Linked Data into account. With hundreds of thousands of datasets out in the world today the re- sults of SemanticWeb evaluations are less generalizable than they should and—this paper argues—can be. This paper describes LOD Lab: a fun- damentally different evaluation paradigm that makes algorithmic evalu- ation against hundreds of thousands of datasets the new norm. LOD Lab is implemented in terms of the existing LOD Laundromat architecture combined with the new open-source programming interface Frank that supportsWeb-scale evaluations to be run from the command-line.We il- lustrate the viability of the LOD Lab approach by rerunning experiments from three recent SemanticWeb research publications and expect it will contribute to improving the quality and reproducibility of experimental work in the Semantic Web community. We show that simply rerunning existing experiments within this new evaluation paradigm brings up in- teresting research questions as to how algorithmic performance relates to (structural) properties of the data. 1",
  author    = "Laurens Rietveld and Wouter Beek and Stefan Schlobach",
  year      = "2015",
  doi       = "10.1007/978-3-319-25010-6_23",
  isbn      = "9783319250090",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "339--355",
  booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}

Contemporary Semantic Web research is in the business of optimizing algorithms for only a handful of datasets such as DBpedia, BSBM, DBLP and only a few more. This means that current practice does not generally take the true variety of Linked Data into account. With hundreds of thousands of datasets out in the world today the re- sults of SemanticWeb evaluations are less generalizable than they should and—this paper argues—can be. This paper describes LOD Lab: a fun- damentally different evaluation paradigm that makes algorithmic evalu- ation against hundreds of thousands of datasets the new norm. LOD Lab is implemented in terms of the existing LOD Laundromat architecture combined with the new open-source programming interface Frank that supportsWeb-scale evaluations to be run from the command-line.We il- lustrate the viability of the LOD Lab approach by rerunning experiments from three recent SemanticWeb research publications and expect it will contribute to improving the quality and reproducibility of experimental work in the Semantic Web community. We show that simply rerunning existing experiments within this new evaluation paradigm brings up in- teresting research questions as to how algorithmic performance relates to (structural) properties of the data. 1

@inbook{003059bab43a4cfea14b6f9c81792be7,
  title     = "LOD Lab: Experiments at LOD Scale",
  author    = "L.J. Rietveld and W.G.J. Beek and S. Schlobach",
  year      = "2015",
  pages     = "339--355",
  booktitle = "The Semantic Web-ISWC 2015",
  publisher = "Springer International Publishing",
}

@misc{10773ee446144f8889d2a541e44b4469,
  title  = "LOD Lab: Experiments at LOD Scale (Compressed Contribution)",
  author = "L.J. Rietveld and W.G.J. Beek and S. Schlobach",
  note   = "Proceedings title: Proceedings of BNAIC 2015",
  year   = "2015",
}

@misc{bf34a1ca29a24eefbed37092286f292a,
  title    = "LOTUS: Linked open text unleashed",
  abstract = "It is dificult to find resources on the Semantic Web today, in particular if one wants to search for resources based on natural language keywords and across multiple datasets. In this paper, we present LOTUS: Linked Open Text UnleaShed, a full-text lookup index over a huge Linked Open Data collection. We detail LOTUS' approach, its implementation, its coverage, and demonstrate the ease with which it allows the LOD cloud to be queried in different domain-specific scenarios.",
  keywords = "Big Data, Findability, Semantic Search, Text Indexing",
  author   = "F. Ilievski and Wouter Beek and {Van Erp}, Marieke and Laurens Rietveld and Stefan Schlobach",
  year     = "2015",
}

It is dificult to find resources on the Semantic Web today, in particular if one wants to search for resources based on natural language keywords and across multiple datasets. In this paper, we present LOTUS: Linked Open Text UnleaShed, a full-text lookup index over a huge Linked Open Data collection. We detail LOTUS' approach, its implementation, its coverage, and demonstrate the ease with which it allows the LOD cloud to be queried in different domain-specific scenarios.

@inbook{0bddd0fa7d2745f4b659b060e1597345,
  title     = "LOTUS: Linked Open Text UnleaShed",
  author    = "F. Ilievski and W.G.J. Beek and {Van Erp}, M. and L.J. Rietveld and K.S. Schlobach",
  year      = "2015",
  booktitle = "COLD workshop at ESWC",
}

@misc{d58f9739dfb04c9585aadb79a5a0d22c,
  title  = "Named Entity Disambiguation with two-stage coherence optimization",
  author = "F. Ilievski and {Van Erp}, M. and P.T.J.M. Vossen and K.S. Schlobach and W.G.J. Beek",
  note   = "Proceedings title: 25th Meeting of Computational Linguistics in the Netherlands",
  year   = "2015",
}

@article{2bbb756cc8d549ae9cf1e78dc13ed97a,
  title     = "On the Advantage of Using Dedicated Data Mining Techniques to Predict Colorectal Cancer",
  author    = "R. Kop and M. Hoogendoorn and L.N.G Moons and M.E. Numans and {ten Teije}, A.C.M.",
  note      = "Proceedings title: Proceedings of the 15th Conference on Artificial Intelligence in Medicine (AIME 2015) Publisher: Springer Place of publication: Berlin Editors: J.H. Holmes, R. Bellazzi, L. Sacchi, N. Peek",
  year      = "2015",
  volume    = "9105",
  pages     = "133--142",
  journal   = "Lecture Notes in Computer Science",
  issn      = "0302-9743",
  publisher = "Springer Verlag",
}

@misc{f2949c1c28a4488bb10bcdf21d5fed4a,
  title   = "Ontology-based Software Architecture Documentation",
  author  = "{de Graaf}, K.A.",
  note    = "Exacte Wetenschappen Naam instelling promotie: VU University Naam instelling onderzoek: VU University",
  year    = "2015",
  school  = "Vrije Universiteit Amsterdam",
}

@inbook{3436a96fc8344c3994c9bc2da24e972e,
  title     = "Open Source Dutch WordNet",
  author    = "M.C. Postma and P.T.J.M. Vossen",
  year      = "2015",
  booktitle = "25th Meeting of Computational Linguistics in the Netherlands",
}

@article{ebcc6df4bd9e4a40bdf32aecd291a172,
  title     = "Patents as instruments for exploring innovation dynamics: geographic and technological perspectives on “photovoltaic cells”",
  abstract  = "The recently developed Cooperative Patent Classifications of the U.S. Patent and Trade Office (USPTO) and the European Patent Office (EPO) provide new options for an informed delineation of samples in both USPTO data and the Worldwide Patent Statistical Database (PatStat) of EPO. Among the ‘‘technologies for the mitigation of climate change’’ (class Y02), we zoom in on nine material technologies for photovoltaic cells; and focus on one of them (CuInSe2) as a lead case. Two recently developed techniques for making patent maps with interactive overlays—geographical ones using Google Maps and maps based on citation relations among International Patent Classifications (IPC)—are elaborated into dynamic versions that allow for online animations and comparisons by using split screens. Various forms of animation are discussed. The longitudinal development of Rao-Stirling diversity in the IPC-based maps provided us with a heuristics for studying technological diversity in terms of generations of the technology. The longitudinal patterns are clear in USPTO data more than in PatStat data because PatStat aggregates patent information from countries in different stages of technological development, whereas one can expect USPTO patents to be competitive at the technological edge.",
  author    = "L. Leydesdorff and F. Alkemade and G. Heimeriks and R.J. Hoekstra",
  year      = "2015",
  doi       = "10.1007/s11192-014-1447-8",
  volume    = "102",
  pages     = "629--651",
  journal   = "Scientometrics",
  issn      = "0138-9130",
  publisher = "Springer Netherlands",
  number    = "1",
}

The recently developed Cooperative Patent Classifications of the U.S. Patent and Trade Office (USPTO) and the European Patent Office (EPO) provide new options for an informed delineation of samples in both USPTO data and the Worldwide Patent Statistical Database (PatStat) of EPO. Among the ‘‘technologies for the mitigation of climate change’’ (class Y02), we zoom in on nine material technologies for photovoltaic cells; and focus on one of them (CuInSe2) as a lead case. Two recently developed techniques for making patent maps with interactive overlays—geographical ones using Google Maps and maps based on citation relations among International Patent Classifications (IPC)—are elaborated into dynamic versions that allow for online animations and comparisons by using split screens. Various forms of animation are discussed. The longitudinal development of Rao-Stirling diversity in the IPC-based maps provided us with a heuristics for studying technological diversity in terms of generations of the technology. The longitudinal patterns are clear in USPTO data more than in PatStat data because PatStat aggregates patent information from countries in different stages of technological development, whereas one can expect USPTO patents to be competitive at the technological edge.

@inbook{7e35bc89b7d441a499e18607b9905801,
  title     = "Processing of Events in Chinese Medical Guidelines and Automatic Generation of the Semantic Data",
  author    = "Y. Fan and J. Gu and Z. Huang",
  year      = "2015",
  booktitle = "Journal of Chinese Digital Medicine",
}

@inbook{d5855680969b492dbf48f27fc5899a81,
  title     = "Prov-O-Viz - Understanding the Role of Activities in Provenance",
  abstract  = "This paper presents PROV-O-Viz, aWeb-based visualizationtool for PROV-based provenance traces coming from various sources, that leverages Sankey Diagrams to reflect the flow of information through activities.We briefly discuss the advantages of this approach compared to other provenance visualization tools. PROV-O-Viz has already been used to visualize provenance traces generated by very different applications.",
  keywords  = "Information flow, Linked data, Provenance, Reusability, Sankey, Visualization",
  author    = "Rinke Hoekstra and Paul Groth",
  year      = "2015",
  doi       = "10.1007/978-3-319-16462-5_18",
  volume    = "8628",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "215--220",
  booktitle = "Provenance and Annotation of Data and Processes - 5th International Provenance and Annotation Workshop, IPAW 2014, Revised Selected Papers",
}

This paper presents PROV-O-Viz, aWeb-based visualizationtool for PROV-based provenance traces coming from various sources, that leverages Sankey Diagrams to reflect the flow of information through activities.We briefly discuss the advantages of this approach compared to other provenance visualization tools. PROV-O-Viz has already been used to visualize provenance traces generated by very different applications.

@inbook{5a276f356ca0483e9b2a28c47ab7259d,
  title     = "Semantic representation of evidence-based medical guidelines and its use cases",
  author    = "Q. Hu and Z. Huang and Jinguang Gu",
  year      = "2015",
  pages     = "397--404",
  booktitle = "Journal of Wuhan University (Natural Science Edition)",
}

@article{4db74fcd21e94f12a3087524690c0849,
  title     = "Semantics for Big Data",
  author    = "{van Harmelen}, F.A.H. and J.A. Hendler and P. Hitzler and K. Janowicz",
  note      = "PT: J; NR: 3; TC: 0; J9: AI MAG; PG: 2; GA: CG2BK; UT: WOS:000353079700001",
  year      = "2015",
  doi       = "10.1609/aimag.v36i1.2559",
  volume    = "36",
  pages     = "3--4",
  journal   = "The AI Magazine",
  issn      = "0738-4602",
  publisher = "AI Access Foundation",
  number    = "1",
}

@article{ce395ed7fb1e4849b0b97819b268f04f,
  title     = "Semantic Technologies for Historical Research: A Survey",
  abstract  = "During the nineties of the last century, historians and computer scientists created together a research agenda around the life cycle of historical information. It comprised the tasks of creation, design, enrichment, editing, retrieval, analysis and presentation of historical information with help of information technology. They also identified a number of problems and challenges in this field, some of them closely related to semantics and meaning. In this survey paper we study the joint work of historians and computer scientists in the use of Semantic Web methods and technologies in historical research. We analyse to what extent these contributions help in solving the open problems in the agenda of historians, and we describe open challenges and possible lines of research pushing further a still young, but promising, historical Semantic Web.",
  author    = "A. Merono and A. Ashkpour and {van Erp}, M.G.J. and K. Mandemakers and L. Breure and A. Scharnhorst and K.S. Schlobach and {van Harmelen}, F.A.H.",
  year      = "2015",
  doi       = "10.3233/SW-140158",
  volume    = "6",
  pages     = "539--564",
  journal   = "Semantic Web",
  issn      = "1570-0844",
  publisher = "IOS Press",
  number    = "6",
}

During the nineties of the last century, historians and computer scientists created together a research agenda around the life cycle of historical information. It comprised the tasks of creation, design, enrichment, editing, retrieval, analysis and presentation of historical information with help of information technology. They also identified a number of problems and challenges in this field, some of them closely related to semantics and meaning. In this survey paper we study the joint work of historians and computer scientists in the use of Semantic Web methods and technologies in historical research. We analyse to what extent these contributions help in solving the open problems in the agenda of historians, and we describe open challenges and possible lines of research pushing further a still young, but promising, historical Semantic Web.

@book{57a9dfbd0c9d463393881dd4ab7a6853,
  title     = "Techniques of Knowledge Management for the Semantic Web",
  author    = "G Qi and Z. Huang and J. Du",
  year      = "2015",
  isbn      = "9787040437003",
  publisher = "Higher Education Press",
}

@book{874814a36ae64150a962b38011a681f1,
  title     = "The SMS platform - RISIS deliverable",
  author    = "{van den Besselaar}, P.A.A. and A. Khalili and A Idrissou and A. Loizou and {van Harmelen}, F.A.H.",
  year      = "2015",
  publisher = "Vrije Universiteit",
}

@article{5c4ec912a8724128a621d0bda7072d62,
  title     = "To SCRY Linked Data: Extending SPARQL the Easy Way",
  abstract  = "Scientific communities are increasingly publishing datasets on the Web following the Linked Data principles, storing RDF graphs in triplestores and making them available for querying through SPARQL. However, solving domain-specific problems often relies on information that cannot be included in such triplestores. For example, it is virtually impossible to foresee, and precompute, all statistical tests users will want to run on these datasets, especially if data from external triplestores is involved. A straightforward solution is to query the triplestore with SPARQL and compute the required information post-hoc. However, post-hoc scripting is laborious and typically not reusable, and the computed information is not accessible within the original query. Other solutions allow this computation to happen at query time, as with SPARQL Extensible Value Testing (EVT) and Linked Data APIs. However, such approaches can be difficult to apply, due to limited interoperability and poor extensibility. In this paper we present SCRY, the SPARQL compatible service layer, which is a lightweight SPARQL endpoint that interprets parts of basic graph patterns as calls to user defined services. SCRY allows users to incorporate algorithms of arbitrary complexity within standards-compliant SPARQL queries, and to use the generated outputs directly within these same queries. Unlike traditional SPARQL endpoints, the RDF graph against which SCRY resolves its queries is generated at query time, by executing services encoded in the basic graph patterns. SCRY's federation-oriented design allows for easy integration with existing SPARQL endpoints, effectively extending their functionality in a decoupled, tool independent way and allowing the power of SemanticWeb technology to be more easily applied to domain-specific problems.",
  author    = "Bas Stringer and Albert Meroño-Peñuela and Anthonis Loizou and Sanne Abeln and Jaap Heringa",
  year      = "2015",
  volume    = "1501",
  pages     = "8--14",
  journal   = "CEUR workshop proceedings",
  issn      = "1613-0073",
  publisher = "CEUR Workshop Proceedings",
}

Scientific communities are increasingly publishing datasets on the Web following the Linked Data principles, storing RDF graphs in triplestores and making them available for querying through SPARQL. However, solving domain-specific problems often relies on information that cannot be included in such triplestores. For example, it is virtually impossible to foresee, and precompute, all statistical tests users will want to run on these datasets, especially if data from external triplestores is involved. A straightforward solution is to query the triplestore with SPARQL and compute the required information post-hoc. However, post-hoc scripting is laborious and typically not reusable, and the computed information is not accessible within the original query. Other solutions allow this computation to happen at query time, as with SPARQL Extensible Value Testing (EVT) and Linked Data APIs. However, such approaches can be difficult to apply, due to limited interoperability and poor extensibility. In this paper we present SCRY, the SPARQL compatible service layer, which is a lightweight SPARQL endpoint that interprets parts of basic graph patterns as calls to user defined services. SCRY allows users to incorporate algorithms of arbitrary complexity within standards-compliant SPARQL queries, and to use the generated outputs directly within these same queries. Unlike traditional SPARQL endpoints, the RDF graph against which SCRY resolves its queries is generated at query time, by executing services encoded in the basic graph patterns. SCRY's federation-oriented design allows for easy integration with existing SPARQL endpoints, effectively extending their functionality in a decoupled, tool independent way and allowing the power of SemanticWeb technology to be more easily applied to domain-specific problems.

@misc{4bcb545190dd4f6fa9ebc3994f6ab0f7,
  title    = "Updating Compressed Column-Stores",
  abstract = "12638",
  author   = "S.A.B.C. Heman",
  note     = "Exacte Wetenschappen Naam instelling promotie: VU University Naam instelling onderzoek: VU University",
  year     = "2015",
  school   = "Vrije Universiteit Amsterdam",
}

12638

@inbook{39cec9e5b32a474a9a5c5f053031bfd3,
  title     = "USING ONTOLOGY TECHNOLOGY IN BIM SYSTEM",
  author    = "H. Wang and Z. Huang and N. Zhong",
  year      = "2015",
  booktitle = "Journal of Industrial Construction",
}

@inbook{903d15404652483fb5177c158462b43e,
  title     = "What happened to …?” Entity-based Timeline Extraction",
  author    = "T. Caselli and A.S. Fokkens-Zwirello and R. Morante and P.T.J.M. Vossen",
  year      = "2015",
  booktitle = "25th Meeting of Computational Linguistics in the Netherlands",
}

@article{5f009551bb6544cb85bc3b285bbea25d,
  title     = "WYSIWYM - Integrated visualization, exploration and authoring of semantically enriched un-structured content",
  abstract  = "The Semantic Web and Linked Data gained traction in the last years. However, the majority of information still is contained in unstructured documents. This can also not be expected to change, since text, images and videos are the natural way how humans interact with information. Semantic structuring on the other hand enables the (semi-)automatic integration, repurposing, rearrangement of information. NLP technologies and formalisms for the integrated representation of unstructured and semantic content (such as RDFa and Microdata) aim at bridging this semantic gap. However, in order for humans to truly benefit from this integration, we need ways to author, visualize and explore unstructured and semantically enriched content in an integrated manner. In this paper, we present the WYSIWYM (What You See is What You Mean) concept, which addresses this issue and formalizes the binding between semantic representation models and UI elements for authoring, visualizing and exploration. With RDFaCE, Pharmer and conTEXT we present and evaluate three complementary showcases implementing the WYSIWYM concept for different application domains.",
  keywords  = "authoring, exploration, Semantic Web, visual mapping, Visualization, WYSIWYG, WYSIWYM",
  author    = "Ali Khalili and Soren Auer",
  year      = "2015",
  doi       = "10.3233/SW-140157",
  volume    = "6",
  pages     = "259--275",
  journal   = "Semantic Web",
  issn      = "1570-0844",
  publisher = "IOS Press",
  number    = "3",
}

The Semantic Web and Linked Data gained traction in the last years. However, the majority of information still is contained in unstructured documents. This can also not be expected to change, since text, images and videos are the natural way how humans interact with information. Semantic structuring on the other hand enables the (semi-)automatic integration, repurposing, rearrangement of information. NLP technologies and formalisms for the integrated representation of unstructured and semantic content (such as RDFa and Microdata) aim at bridging this semantic gap. However, in order for humans to truly benefit from this integration, we need ways to author, visualize and explore unstructured and semantically enriched content in an integrated manner. In this paper, we present the WYSIWYM (What You See is What You Mean) concept, which addresses this issue and formalizes the binding between semantic representation models and UI elements for authoring, visualizing and exploration. With RDFaCE, Pharmer and conTEXT we present and evaluate three complementary showcases implementing the WYSIWYM concept for different application domains.

@article{5526532ed2bf45d0a35184eb812c11c1,
  title     = "WaaS: Wisdom as a service",
  abstract  = "An emerging hyper-world encompasses all human activities in a social-cyber-physical space. Its power derives from the Wisdom Web of Things (W2T) cycle, namely, 'from things to data, information, knowledge, wisdom, services, humans, and then back to things.'' The W2T cycle leads to a harmonious symbiosis among humans, computers, and things, which can be constructed by large-scale converging of intelligent information technology applications with an open and interoperable architecture. The recent advances in cloud computing, the Internet of Things, Web of Things, Big Data, and other research fields have provided just such an open system architecture with resource sharing and services. The next step is to develop an open and interoperable content architecture with intelligent sharing and services for the organization and transformation in the data, information, knowledge, and wisdom (DIKW) hierarchy. This article introduces wisdom as a service (WaaS), a content architecture based on the pay-as-you-go IT trend. The WaaS infrastructure and the main challenges in WaaS research and applications are discussed. A case study is also described. Relying on cloud computing and big data, WaaS provides a practical approach to realize the W2T cycle in the hyper-world for the coming age of ubiquitous intelligent IT applications.",
  keywords  = "Big Data, cloud computing, hyper-world, intelligent systems, IoT, W2T cycle, WaaS, WoT",
  author    = "Jianhui Chen and Jianhua Ma and Ning Zhong and Yiyu Yao and Jiming Liu and Runhe Huang and Wenbin Li and Zhisheng Huang and Yang Gao and Jianping Cao",
  year      = "2014",
  month     = "11",
  doi       = "10.1109/MIS.2014.19",
  volume    = "29",
  pages     = "40--47",
  journal   = "IEEE Intelligent Systems",
  issn      = "1541-1672",
  publisher = "Institute of Electrical and Electronics Engineers Inc.",
  number    = "6",
}

An emerging hyper-world encompasses all human activities in a social-cyber-physical space. Its power derives from the Wisdom Web of Things (W2T) cycle, namely, 'from things to data, information, knowledge, wisdom, services, humans, and then back to things.'' The W2T cycle leads to a harmonious symbiosis among humans, computers, and things, which can be constructed by large-scale converging of intelligent information technology applications with an open and interoperable architecture. The recent advances in cloud computing, the Internet of Things, Web of Things, Big Data, and other research fields have provided just such an open system architecture with resource sharing and services. The next step is to develop an open and interoperable content architecture with intelligent sharing and services for the organization and transformation in the data, information, knowledge, and wisdom (DIKW) hierarchy. This article introduces wisdom as a service (WaaS), a content architecture based on the pay-as-you-go IT trend. The WaaS infrastructure and the main challenges in WaaS research and applications are discussed. A case study is also described. Relying on cloud computing and big data, WaaS provides a practical approach to realize the W2T cycle in the hyper-world for the coming age of ubiquitous intelligent IT applications.

@inbook{d2310ad12c5c4c5db6aa7eca3c9ab94c,
  title     = "A Conceptual Model for Detecting Interactions among Medical Recommendations in Clinical Guidelines",
  abstract  = "Representation of clinical knowledge is still an open research topic. In particular, classical languages designed for representing clinical guidelines, which were meant for producing diagnostic and treatment plans, present limitations such as for re-using, combining, and reasoning over existing knowledge. In this paper, we address such limitations by proposing an extension of the TMR conceptual model to represent clinical guidelines that allows re-using and combining knowledge from several guidelines to be applied to patients with multimorbidities. We provide means to (semi)automatically detect interactions among recommendations that require some attention from experts, such as recommending more than once the same drug. We evaluate the model by applying it to a realistic case study involving 3 diseases (Osteoarthritis, Hypertension and Diabetes) and compare the results with two other existing methods.",
  keywords  = "Clinical knowledge representation, Combining medical guidelines, Multimorbidity, Reasoning",
  author    = "{Carretta Zamborlini}, Veruska and {Da Silveira}, Marcos and Cedric Pruski and Rinke Hoekstra and {ten Teije}, Annette and {van Harmelen}, Frank",
  note      = "Proceedings title: Proceedings of the 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2014) Publisher: Springer Editors: K Janowicz, S Schlobach, S Lambrix, E Hyvonen",
  year      = "2014",
  volume    = "8876",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  pages     = "591--606",
  booktitle = "Knowledge Engineering and Knowledge Management - 19th International Conference, EKAW 2014, Proceedings",
}

Representation of clinical knowledge is still an open research topic. In particular, classical languages designed for representing clinical guidelines, which were meant for producing diagnostic and treatment plans, present limitations such as for re-using, combining, and reasoning over existing knowledge. In this paper, we address such limitations by proposing an extension of the TMR conceptual model to represent clinical guidelines that allows re-using and combining knowledge from several guidelines to be applied to patients with multimorbidities. We provide means to (semi)automatically detect interactions among recommendations that require some attention from experts, such as recommending more than once the same drug. We evaluate the model by applying it to a realistic case study involving 3 diseases (Osteoarthritis, Hypertension and Diabetes) and compare the results with two other existing methods.

@inbook{e5d1e3e7c14c4001b3c0a19e8e714fe7,
  title     = "Advances in Large-Scale RDF Data Management",
  author    = "P.A. Boncz and O. Erling and M.D. Pham",
  year      = "2014",
  series    = "LNCS 8661",
  publisher = "Springer",
  pages     = "21--44",
  booktitle = "Linked Open Data",
}

@inbook{38890af079984fd4ae0fcb9ceed8dfe1,
  title     = "A Network Analysis of Dutch Regulations",
  author    = "R.J. Hoekstra",
  year      = "2014",
  isbn      = "9788849527698",
  editor    = "Radboud Winkels and Nicola Lettieri and Sebastiano Faro",
  booktitle = "Network Analysis in Law",
}

@article{2e5460ceafef430e8b4f948f915bf90c,
  title     = "API-centric Linked Data Integration: The Open PHACTS Discovery Platform Case Study",
  abstract  = "Data integration is a key challenge faced in pharmacology where there are numerous heterogeneous databases spanning multiple domains (e.g. chemistry and biology). To address this challenge, the Open PHACTS consortium has developed the Open PHACTS Discovery Platform that leverages Linked Data to provide integrated access to pharmacology databases. Between its launch in April 2013 and March 2014, the platform has been accessed over 13.5 million times and has multiple applications that integrate with it. In this work, we discuss how Application Programming Interfaces can extend the classical Linked Data Application Architecture to facilitate data integration. Additionally, we show how the Open PHACTS Discovery Platform implements this extended architecture.",
  author    = "P.T. Groth and A. Loizou and A.J.G Gray and C.A. Goble and L Harland and S Pettifier",
  year      = "2014",
  doi       = "10.1016/j.websem.2014.03.003",
  journal   = "Journal of Web Semantics",
  issn      = "1570-8268",
  publisher = "Elsevier",
}

Data integration is a key challenge faced in pharmacology where there are numerous heterogeneous databases spanning multiple domains (e.g. chemistry and biology). To address this challenge, the Open PHACTS consortium has developed the Open PHACTS Discovery Platform that leverages Linked Data to provide integrated access to pharmacology databases. Between its launch in April 2013 and March 2014, the platform has been accessed over 13.5 million times and has multiple applications that integrate with it. In this work, we discuss how Application Programming Interfaces can extend the classical Linked Data Application Architecture to facilitate data integration. Additionally, we show how the Open PHACTS Discovery Platform implements this extended architecture.

@article{da523417e0894dfb85435688a3609874,
  title     = "Applying linked data approaches to pharmacology: Architectural decisions and implementation",
  abstract  = "The discovery of new medicines requires pharmacologists to interact with a number of information sources ranging from tabular data to scientific papers, and other specialized formats. In this application report, we describe a linked data platform for integrating multiple pharmacology datasets that form the basis for several drug discovery applications. The functionality offered by the platform has been drawn from a collection of prioritised drug discovery business questions created as part of the Open PHACTS project, a collaboration of research institutions and major pharmaceutical companies. We describe the architecture of the platform focusing on seven design decisions that drove its development with the aim of informing others developing similar software in this or other domains. The utility of the platform is demonstrated by the variety of drug discovery applications being built to access the integrated data. An alpha version of the OPS platform is currently available to the Open PHACTS consortium and a first public release will be made in late 2012, see http://www.openphacts.org/ for details. © 2014 - IOS Press and the authors.",
  author    = "A.J.G Gray and P.T. Groth and A. Loizou and S Askjaer and C Brenninkmeijer and K. Burger and C. Chichester and C.T. Evelo and C.A. Goble and L Harland and S Pettifier and M. Thompson and A Waagmeester and A.J William",
  year      = "2014",
  doi       = "10.3233/SW-2012-0088",
  volume    = "5",
  pages     = "101--114",
  journal   = "Semantic Web",
  issn      = "1570-0844",
  publisher = "IOS Press",
  number    = "2",
}

The discovery of new medicines requires pharmacologists to interact with a number of information sources ranging from tabular data to scientific papers, and other specialized formats. In this application report, we describe a linked data platform for integrating multiple pharmacology datasets that form the basis for several drug discovery applications. The functionality offered by the platform has been drawn from a collection of prioritised drug discovery business questions created as part of the Open PHACTS project, a collaboration of research institutions and major pharmaceutical companies. We describe the architecture of the platform focusing on seven design decisions that drove its development with the aim of informing others developing similar software in this or other domains. The utility of the platform is demonstrated by the variety of drug discovery applications being built to access the integrated data. An alpha version of the OPS platform is currently available to the Open PHACTS consortium and a first public release will be made in late 2012, see http://www.openphacts.org/ for details. © 2014 - IOS Press and the authors.

@inbook{3fbc7f197cde4e729501efbdf251c04c,
  title     = "A Pragmatic Semantics for Identity in Linked Data",
  author    = "W.G.J. Beek and K.S. Schlobach and {van Harmelen}, F.A.H.",
  year      = "2014",
  booktitle = "The 14th International Conference on Principles of Knowledge Representation and Reasoning",
}

@inbook{23a86df0d2f94f6cac3242ea35fe3a44,
  title     = "A Web Observatory for the Machine Processability of Structured Data on the Web",
  author    = "W.G.J. Beek and P.T. Groth and K.S. Schlobach and R.J. Hoekstra",
  year      = "2014",
  pages     = "249--250",
  booktitle = "Proceedings of the 2014 ACM Conference on Web Science",
}

@article{fabc3899a46a41c5870f495c1aef750f,
  title   = "Benchmarking Linked Open Data Management Systems",
  author  = "{Angles Rojas}, R. and M.D. Pham and P.A. Boncz",
  year    = "2014",
  volume  = "96",
  pages   = "24--25",
  journal = "ERCIM NEWS",
  issn    = "0926-4981",
}

@misc{ab082add992348c7b7e737da24b5855f,
  title   = "Computing Healthcare Quality Indicators Automatically: Secondary Use of Patient Data and Semantic Interoperability",
  author  = "K. Dentler",
  note    = "Naam instelling promotie: VU Vrije Universiteit Naam instelling onderzoek: VU Vrije Universiteit",
  year    = "2014",
  school  = "Vrije Universiteit Amsterdam",
}

@article{104f96ec6d1b4495a7fe2d6887d1b81f,
  title     = "Conflict Resolution in Partially Ordered OWL DL Ontologies",
  abstract  = "Inconsistency handling in OWL DL ontologies is an important problem because an ontology can easily be inconsistent when it is generated or modified. Current approaches to dealing with inconsistent ontologies often assume that there exists a total order over axioms and use such an order to select axioms to remove. However, in some cases, such as ontology merging, a total order may not be available and we only have a partial order over axioms. In this paper, we consider a general notion of logical inconsistency and define the notion of conflict of an inconsistent ontology. We then propose a general approach to resolving inconsistency of a partially ordered ontology. We instantiate this approach by proposing two algorithms to calculate prioritized hitting sets for a set of conflicts. We implement the algorithms and provide evaluation results on the efficiency and effectiveness by considering both artificial and real-life data sets.",
  author    = "Q. Ji and Z. Gao and Z. Huang",
  note      = "Proceedings title: Proceedings of 21st European Conference on Artificial Intelligence (ECAI2014) Publisher: IOS Press",
  year      = "2014",
  doi       = "10.3233/978-1-61499-419-0-471",
  volume    = "263",
  pages     = "471--476",
  journal   = "Frontiers in Artificial Intelligence and Applications",
  issn      = "0922-6389",
  publisher = "IOS Press",
}

Inconsistency handling in OWL DL ontologies is an important problem because an ontology can easily be inconsistent when it is generated or modified. Current approaches to dealing with inconsistent ontologies often assume that there exists a total order over axioms and use such an order to select axioms to remove. However, in some cases, such as ontology merging, a total order may not be available and we only have a partial order over axioms. In this paper, we consider a general notion of logical inconsistency and define the notion of conflict of an inconsistent ontology. We then propose a general approach to resolving inconsistency of a partially ordered ontology. We instantiate this approach by proposing two algorithms to calculate prioritized hitting sets for a set of conflicts. We implement the algorithms and provide evaluation results on the efficiency and effectiveness by considering both artificial and real-life data sets.

@inbook{044e7a11b506487bb4b1a03d7463bbe4,
  title     = "Constructing Provenance Cubes Based on Semantic Neuroimaging Data Provenances",
  author    = "J. Chen and J Feng and N. Zhong and Z. Huang",
  year      = "2014",
  booktitle = "Proceedings of 8th China Semantic Web Symposium & 3rd Web Science Conference (CSWS2014)",
}

@book{63278e6c72b044e885222949522971f2,
  title     = "Designing engines for data analysis",
  author    = "P.A. Boncz",
  note      = "Inaugurele rede - 2014-10-17",
  year      = "2014",
  publisher = "Vrije Universiteit Amsterdam",
}

@inbook{fbdbf7048b784b69abd61d45c084b6e8,
  title     = "Detecting Stay Areas from a User's Mobile Phone Data for Urban Computing",
  author    = "H. Wang and N. Zhong and Z. Huang and J. Huang and E. Zhou",
  year      = "2014",
  pages     = "336--346",
  booktitle = "The 2014 International Conference on Active Media Technology",
  publisher = "Springer",
}

@article{d8abe7a803604a129c1f71a9101754a3,
  title     = "Drug discovery FAQs: workflows for answering multidomain drug discovery questions",
  abstract  = "Modern data-driven drug discovery requires integrated resources to support decision-making and enable new discoveries. The Open PHACTS Discovery Platform (http://dev.openphacts.org) was built to address this requirement by focusing on drug discovery questions that are of high priority to the pharmaceutical industry. Although complex, most of these frequently asked questions (FAQs) revolve around the combination of data concerning compounds, targets, pathways and diseases. Computational drug discovery using workflow tools and the integrated resources of Open PHACTS can deliver answers to most of these questions. Here, we report on a selection of workflows used for solving these use cases and discuss some of the research challenges. The workflows are accessible online from myExperiment (http://www.myexperiment.org) and are available for reuse by the scientific community.",
  author    = "C. Chichester and D. Digles and R.M. Siebes and A. Loizou and P.T. Groth and L Harland",
  year      = "2014",
  doi       = "10.1016/j.drudis.2014.11.006",
  journal   = "Drug Discovery Today",
  issn      = "1359-6446",
  publisher = "Elsevier Limited",
}

Modern data-driven drug discovery requires integrated resources to support decision-making and enable new discoveries. The Open PHACTS Discovery Platform (http://dev.openphacts.org) was built to address this requirement by focusing on drug discovery questions that are of high priority to the pharmaceutical industry. Although complex, most of these frequently asked questions (FAQs) revolve around the combination of data concerning compounds, targets, pathways and diseases. Computational drug discovery using workflow tools and the integrated resources of Open PHACTS can deliver answers to most of these questions. Here, we report on a selection of workflows used for solving these use cases and discuss some of the research challenges. The workflows are accessible online from myExperiment (http://www.myexperiment.org) and are available for reuse by the scientific community.

@inbook{9700d89776784a4d81bfe8f2985049a3,
  title     = "Evidence-based clinical guidelines in SemanticCT",
  author    = "Q. Hu and Z. Huang and {van Harmelen}, F.A.H. and {ten Teije}, A.C.M. and Jinguang Gu",
  year      = "2014",
  booktitle = "8th China Semantic Web Symposium & 3rd Web Science Conference (CSWS2014)",
}

@inbook{743083cf4b644e20a6146f1ac7d964c9,
  title     = "Feasibility Estimation for Clinical Trials",
  author    = "Z. Huang and {van Harmelen}, F.A.H. and {ten Teije}, A.C.M. and A. Dekker",
  year      = "2014",
  pages     = "68--77",
  booktitle = "Proceedings of the 7th International Conference on Health Informatics (HEALTHINF2014).",
  publisher = "scitepress digital library",
}

@article{d2f427f3875c41fe90b71a3fc5ba3dc4,
  title     = "Formalization and computation of quality measures based on electronic medical records",
  abstract  = "Objective: Ambiguous definitions of quality measures in natural language impede their automated computability and also the reproducibility, validity, timeliness, traceability, comparability, and interpretability of computed results. Therefore, quality measures should be formalized before their release. We have previously developed and successfully applied a method for clinical indicator formalization (CLIF). The objective of our present study is to test whether CLIF is generalizable- that is, applicable to a large set of heterogeneous measures of different types and from various domains. Materials and methods: We formalized the entire set of 159 Dutch quality measures for general practice, which contains structure, process, and outcome measures and covers seven domains. We relied on a web-based tool to facilitate the application of our method. Subsequently, we computed the measures on the basis of a large database of real patient data. Results: Our CLIF method enabled us to fully formalize 100% of the measures. Owing to missing functionality, the accompanying tool could support full formalization of only 86% of the quality measures into Structured Query Language (SQL) queries. The remaining 14% of the measures required manual application of our CLIF method by directly translating the respective criteria into SQL. The results obtained by computing the measures show a strong correlation with results computed independently by two other parties. Conclusions: The CLIF method covers all quality measures after having been extended by an additional step. Our web tool requires further refinement for CLIF to be applied completely automatically. We therefore conclude that CLIF is sufficiently generalizable to be able to formalize the entire set of Dutch quality measures for general practice.",
  author    = "K. Dentler and M.E. Numans and {ten Teije}, A.C.M. and R. Cornet and {de Keizer}, N.F.",
  year      = "2014",
  doi       = "10.1136/amiajnl-2013-001921",
  volume    = "21",
  pages     = "285--291",
  journal   = "Journal of the American Medical Informatics Association",
  issn      = "1067-5027",
  publisher = "Oxford University Press",
  number    = "2",
}

Objective: Ambiguous definitions of quality measures in natural language impede their automated computability and also the reproducibility, validity, timeliness, traceability, comparability, and interpretability of computed results. Therefore, quality measures should be formalized before their release. We have previously developed and successfully applied a method for clinical indicator formalization (CLIF). The objective of our present study is to test whether CLIF is generalizable- that is, applicable to a large set of heterogeneous measures of different types and from various domains. Materials and methods: We formalized the entire set of 159 Dutch quality measures for general practice, which contains structure, process, and outcome measures and covers seven domains. We relied on a web-based tool to facilitate the application of our method. Subsequently, we computed the measures on the basis of a large database of real patient data. Results: Our CLIF method enabled us to fully formalize 100% of the measures. Owing to missing functionality, the accompanying tool could support full formalization of only 86% of the quality measures into Structured Query Language (SQL) queries. The remaining 14% of the measures required manual application of our CLIF method by directly translating the respective criteria into SQL. The results obtained by computing the measures show a strong correlation with results computed independently by two other parties. Conclusions: The CLIF method covers all quality measures after having been extended by an additional step. Our web tool requires further refinement for CLIF to be applied completely automatically. We therefore conclude that CLIF is sufficiently generalizable to be able to formalize the entire set of Dutch quality measures for general practice.

@inbook{6c0060454640430fb3856e1dd017e2f5,
  title     = "Generating Scientific Documentation for Computational Experiments Using Provenance",
  author    = "A. Wibisono and Peter Bloem and {de Vries}, G.K.D and P.T. Groth and A. Belloum and M. Bubak",
  year      = "2014",
  booktitle = "5th International Provenance and Annotation Workshop (IPAW'14)",
}

@inbook{990518b0126a4f4eaff2b7c30046ee4d,
  title     = "How to generate query parameters in RDF benchmarks?",
  author    = "A. Gubichev and {Angles Rojas}, R. and P.A. Boncz",
  year      = "2014",
  booktitle = "Proceedings of the Data Engineering meets the Semantic Web (DESWEB, 2014)",
}

@article{b73c2d7620a24b8da05009bf44940434,
  title     = "Influence of data quality on computed Dutch hospital quality indicators: a case study in colorectal cancer surgery.",
  abstract  = "Background: Our study aims to assess the influence of data quality on computed Dutch hospital quality indicators, and whether colorectal cancer surgery indicators can be computed reliably based on routinely recorded data from an electronic medical record (EMR). Methods. Cross-sectional study in a department of gastrointestinal oncology in a university hospital, in which a set of 10 indicators is computed (1) based on data abstracted manually for the national quality register Dutch Surgical Colorectal Audit (DSCA) as reference standard and (2) based on routinely collected data from an EMR. All 75 patients for whom data has been submitted to the DSCA for the reporting year 2011 and all 79 patients who underwent a resection of a primary colorectal carcinoma in 2011 according to structured data in the EMR were included. Comparison of results, investigating the causes for any differences based on data quality analysis. Main outcome measures are the computability of quality indicators, absolute percentages of indicator results, data quality in terms of availability in a structured format, completeness and correctness. Results: All indicators were fully computable based on the DSCA dataset, but only three based on EMR data, two of which were percentages. For both percentages, the difference in proportions computed based on the two datasets was significant.All required data items were available in a structured format in the DSCA dataset. Their average completeness was 86%, while the average completeness of these items in the EMR was 50%. Their average correctness was 87%. Conclusions: Our study showed that data quality can significantly influence indicator results, and that our EMR data was not suitable to reliably compute quality indicators. EMRs should be designed in a way so that the data required for audits can be entered directly in a structured and coded format. © 2014 Dentler et al.; licensee BioMed Central Ltd.",
  author    = "K. Dentler and R. Cornet and {ten Teije}, A.C.M. and P. Tanis and J. Klinkenbijl and K. Tytgat and {de Keizer}, N.F.",
  year      = "2014",
  doi       = "10.1186/1472-6947-14-32",
  volume    = "14",
  journal   = "BMC Medical Informatics and Decision Making",
  issn      = "1472-6947",
  publisher = "BioMed Central",
  number    = "32",
}

Background: Our study aims to assess the influence of data quality on computed Dutch hospital quality indicators, and whether colorectal cancer surgery indicators can be computed reliably based on routinely recorded data from an electronic medical record (EMR). Methods. Cross-sectional study in a department of gastrointestinal oncology in a university hospital, in which a set of 10 indicators is computed (1) based on data abstracted manually for the national quality register Dutch Surgical Colorectal Audit (DSCA) as reference standard and (2) based on routinely collected data from an EMR. All 75 patients for whom data has been submitted to the DSCA for the reporting year 2011 and all 79 patients who underwent a resection of a primary colorectal carcinoma in 2011 according to structured data in the EMR were included. Comparison of results, investigating the causes for any differences based on data quality analysis. Main outcome measures are the computability of quality indicators, absolute percentages of indicator results, data quality in terms of availability in a structured format, completeness and correctness. Results: All indicators were fully computable based on the DSCA dataset, but only three based on EMR data, two of which were percentages. For both percentages, the difference in proportions computed based on the two datasets was significant.All required data items were available in a structured format in the DSCA dataset. Their average completeness was 86%, while the average completeness of these items in the EMR was 50%. Their average correctness was 87%. Conclusions: Our study showed that data quality can significantly influence indicator results, and that our EMR data was not suitable to reliably compute quality indicators. EMRs should be designed in a way so that the data required for audits can be entered directly in a structured and coded format. © 2014 Dentler et al.; licensee BioMed Central Ltd.

@inbook{f8b9e534ae1f4e73a2640aeaa020ba40,
  title     = "Integrating Brain Big Data with Depression Diagnostic Information by DataBrain Method",
  author    = "T. Kotake and J. Chen and N. Zhong and H. Zhong and J. Han and Z. Huang and Y. Yang",
  year      = "2014",
  booktitle = "Proceedings of 2014 International Conference on Brain Informatics and Health(BIH2014)",
  publisher = "Springer",
}

@book{61b369b6e8724971ae4f2327b65a7be5,
  title     = "Knowledge Engineering and Knowledge Management: 19th International Conference, EKAW 2014",
  author    = "K. Janowicz and K.S. Schlobach and P. Lambrix and E. Hyvönen",
  year      = "2014",
  publisher = "Springer",
}

@book{6d7510512e6a4df19cab7da812b79d6f,
  title     = "Knowledge Representation for Health Care (6th International Workshop, KR4HC 2014)",
  author    = "S Miksch and D Riano and {ten Teije}, A.C.M.",
  year      = "2014",
  doi       = "10.1007/978-3-319-13281-5",
  isbn      = "9783319132815",
  series    = "Lecture Notes CS",
  publisher = "Springer",
  number    = "Vol. 8903",
}

@inbook{9a5b54119a574d658275326369fbdd21,
  title     = "LDBC: benchmarks for graph and RDF data management",
  author    = "P.A. Boncz",
  year      = "2014",
  booktitle = "International Database Engineering and Applications Symposium (IDEAS 2014)",
}

@book{d291f7e9825642388f60aad7cb11fec6,
  title     = "Legal Knowledge and Information Systems - The Twenty Seventh Annual Conference (JURIX 2014)",
  author    = "R.J. Hoekstra",
  note      = "Gebeurtenis: JURIX 2014",
  year      = "2014",
  isbn      = "9781614994671",
  series    = "Frontiers in Artificial Intelligence",
  publisher = "IOS Press",
}

@misc{9fc0a0c2a1364e01ab31084f6674584e,
  title  = "Linking the STCN and Performing Big Data Queries in the Humanities",
  author = "W.G.J. Beek and R.J. Hoekstra and F. Maas and A. Merono and I.B. Leemans",
  note   = "Proceedings title: Digital Humanities Benelux Conference 2014 Place of publication: The Hague",
  year   = "2014",
}

@inbook{0ff21dea26bc4ae197cfe5235c110ca1,
  title     = "Linkitup: Semantic Publishing of Research Data",
  author    = "R.J. Hoekstra and P.T. Groth and M. Charalganov",
  year      = "2014",
  editor    = "V Presutti and M Stankovic and E Cambria",
  booktitle = "Proceedings of the Semantic Web Evaluation Challenge at ESWC 2014",
  publisher = "Springer",
}

@inbook{68bd3872218f425eb23543a970e6a3f2,
  title     = "LOD laundromat: A uniform way of publishing other people’s dirty data",
  author    = "W.G.J. Beek and L.J. Rietveld and H. Bazoobandi and J. Wielemaker and K.S. Schlobach",
  year      = "2014",
  pages     = "213--228",
  booktitle = "The Semantic Web–ISWC 2014",
  publisher = "Springer International Publishing",
}

@misc{29b151ebee5c48ddbb483e98b5ab48a3,
  title  = "LOD Laundromat: A Uniform Way of Publishing Other People's Dirty Data.",
  author = "{van Beek}, W. and L.J. Rietveld and H. Bazoobandi and J. Wielemaker and K.S. Schlobach",
  year   = "2014",
}

@inbook{2c868bd438b442baab7b42ba4cb4c98f,
  title     = "LOD Laundromat: A Uniform Way of Publishing Other People's Dirty Data.",
  author    = "W.G.J. Beek and L.J. Rietveld and H. Bazoubandi and J. Wielemaker and K.S. Schlobach",
  year      = "2014",
  booktitle = "The Semantic Web–ISWC 2014",
}

@inbook{f2635d22c7e040b9872801bbb75e9a13,
  title     = "LOD Laundromat: Extended Abstract",
  author    = "W.G.J. Beek and L.J. Rietveld and H. Bazoubandi and J. Wielemaker and K.S. Schlobach",
  year      = "2014",
  booktitle = "Proceedings of the Belgisch-Nederlandse Kunstmatige Intelligentie Conferentie (BNAIC) 2014",
}

@misc{181a1e36dced43c79026afd93cbb73a8,
  title  = "Man vs. Machine: Differences in SPARQL Queries.",
  author = "L.J. Rietveld and R.J. Hoekstra",
  year   = "2014",
}

@article{3bddd5d360c14fae949a6caef6ffe852,
  title     = "Measuring effectiveness of ontology debugging systems",
  author    = "J. Qiu and Z. Gao and Z. Huang and Man Zhu",
  year      = "2014",
  volume    = "11",
  journal   = "Knowledge-Based Systems",
  issn      = "0950-7051",
  publisher = "Elsevier",
}

@article{de979c20a4974979b1567ad4056b9cf9,
  title   = "MonetDB/RDF: Discovering and Exploiting the Emergent Schema of RDF Data",
  author  = "M.D. Pham and P.A. Boncz",
  year    = "2014",
  volume  = "96",
  pages   = "41--42",
  journal = "ERCIM NEWS",
  issn    = "0926-4981",
}

@inbook{796639283c6e4c59b8807f555a7a0ccb,
  title     = "Morsel-Driven Parallelism: A NUMA-Aware Query Evaluation Framework for the Many-Core Age",
  author    = "V. Leis and P.A. Boncz and T. Neumann and A. Kemper",
  year      = "2014",
  booktitle = "Proc. ACM SIGMOD",
}

@inbook{79b478241fc6419187d67dee14a66d3e,
  title     = "Parameter Curation for Benchmark Queries",
  abstract  = "In this paper we consider the problem of generating parameters for benchmark queries so these have stable behavior despite being executed on datasets (real-world or synthetic) with skewed data distributions and value correlations. We show that uniform random sampling of the substitution parameters is not well suited for such benchmarks, since it results in unpredictable runtime behavior of queries. We present our approach of Parameter Curation with the goal of selecting parameter bindings that have consistently low-variance intermediate query result sizes throughout the query plan. Our solution is illustrated with IMDB data and the recently proposed LDBC Social Network Benchmark (SNB).",
  author    = "Andrey Gubichev and Peter Boncz",
  year      = "2014",
  doi       = "10.1007/978-3-319-15350-6_8",
  volume    = "8904",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "113--129",
  booktitle = "Performance Characterization and Benchmarking: Traditional to Big Data - 6th TPC Technology Conference, TPCTC 2014, Revised Selected Papers",
}

In this paper we consider the problem of generating parameters for benchmark queries so these have stable behavior despite being executed on datasets (real-world or synthetic) with skewed data distributions and value correlations. We show that uniform random sampling of the substitution parameters is not well suited for such benchmarks, since it results in unpredictable runtime behavior of queries. We present our approach of Parameter Curation with the goal of selecting parameter bindings that have consistently low-variance intermediate query result sizes throughout the query plan. Our solution is illustrated with IMDB data and the recently proposed LDBC Social Network Benchmark (SNB).

@article{602fdc1b78e94ddfaece272249d7b258,
  title     = "Querying neXtProt nanopublications and their value for insights on sequence variants and tissue expression",
  abstract  = "Understanding how genetic differences between individuals impact the regulation, expression, and ultimately function of proteins is an important step toward realizing the promise of personal medicine. There are several technical barriers hindering the transition of biological knowledge into the applications relevant to precision medicine. One important challenge for data integration is that new biological sequences (proteins, DNA) have multiple issues related to interoperability potentially creating a quagmire in the published data, especially when different data sources do not appear to be in agreement. Thus, there is an urgent need for systems and methodologies to facilitate the integration of information in a uniform manner to allow seamless querying of multiple data types which can illuminate, for example, the relationships between protein modifications and causative genomic variants. Our work demonstrates for the first time how semantic technologies can be used to address these challenges using the nanopublication model applied to the neXtProt data set, a curated knowledgebase of information about human proteins. We have applied the nanopublication model to demonstrate querying over several named graphs, including the provenance information associated with the curated scientific assertions from neXtProt. We show by way of use cases using sequence variations, post-translational modifications (PTMs) and tissue expression, that querying the neXtProt nanopublication implementation is a credible approach for expanding biological insight.",
  author    = "C. Chichester and P. Gaudet and R. Karch and P.T. Groth and L Lane and A Bairoch and B Mons and A. Loizou",
  year      = "2014",
  doi       = "10.1016/j.websem.2014.05.001",
  journal   = "Journal of Web Semantics",
  issn      = "1570-8268",
  publisher = "Elsevier",
}

Understanding how genetic differences between individuals impact the regulation, expression, and ultimately function of proteins is an important step toward realizing the promise of personal medicine. There are several technical barriers hindering the transition of biological knowledge into the applications relevant to precision medicine. One important challenge for data integration is that new biological sequences (proteins, DNA) have multiple issues related to interoperability potentially creating a quagmire in the published data, especially when different data sources do not appear to be in agreement. Thus, there is an urgent need for systems and methodologies to facilitate the integration of information in a uniform manner to allow seamless querying of multiple data types which can illuminate, for example, the relationships between protein modifications and causative genomic variants. Our work demonstrates for the first time how semantic technologies can be used to address these challenges using the nanopublication model applied to the neXtProt data set, a curated knowledgebase of information about human proteins. We have applied the nanopublication model to demonstrate querying over several named graphs, including the provenance information associated with the curated scientific assertions from neXtProt. We show by way of use cases using sequence variations, post-translational modifications (PTMs) and tissue expression, that querying the neXtProt nanopublication implementation is a credible approach for expanding biological insight.

@article{91cb952159614b12b8d6ff7fff464f22,
  title     = "Reports on the 2013 AAAI fall symposium series",
  abstract  = "The Association for the Advancement of Artificial Intelligence was pleased to present the 2013 Fall Symposium Series, held Friday through Sunday, November 15-17, at the Westin Arlington Gateway in Arlington, Virginia, near Washington, D.C., USA. The titles of the five symposia were Discovery Informatics: AI Takes a Science- Centered View on Big Data (FS-13-01); How Should Intelligence Be Abstracted in AI Research: MDPs, Symbolic Representations, Artificial Neural Networks, or - ? (FS-13-02); Integrated Cognition (FS-13-03); Semantics for Big Data (FS- 13-04); and Social Networks and Social Contagion: Web Analytics and Computational Social Science (FS-13-05). The highlights of each symposium are presented in this report.",
  author    = "Burns, {Gully A P C} and Yolanda Gil and Natalia Villanueva-Rosales and Yan Liu and Sebastian Risi and Joel Lehman and Jeff Clune and Christian Lebiere and Rosenbloom, {Paul S.} and {Van Harmelen}, Frank and Hendler, {James A.} and Pascal Hitzler and Krzysztof Janowicz and Samarth Swarup",
  year      = "2014",
  volume    = "35",
  pages     = "69--74",
  journal   = "The AI Magazine",
  issn      = "0738-4602",
  publisher = "AI Access Foundation",
  number    = "2",
}

The Association for the Advancement of Artificial Intelligence was pleased to present the 2013 Fall Symposium Series, held Friday through Sunday, November 15-17, at the Westin Arlington Gateway in Arlington, Virginia, near Washington, D.C., USA. The titles of the five symposia were Discovery Informatics: AI Takes a Science- Centered View on Big Data (FS-13-01); How Should Intelligence Be Abstracted in AI Research: MDPs, Symbolic Representations, Artificial Neural Networks, or - ? (FS-13-02); Integrated Cognition (FS-13-03); Semantics for Big Data (FS- 13-04); and Social Networks and Social Contagion: Web Analytics and Computational Social Science (FS-13-05). The highlights of each symposium are presented in this report.

@inbook{56aeef4da7c8498eac1532de8dfedce5,
  title     = "Rough set semantics for identity on the Web",
  abstract  = "Identity relations are at the foundation of many logic-based knowledge representations. We argue that the traditional notion of equality, is unsuited for many realistic knowledge representation settings. The classical interpretation of equality is too strong when the equality statements are re-used outside their original context. On the Semantic Web, equality statements are used to interlink multiple descriptions of the same object, using owl:sameAs assertions. And indeed, many practical uses of owl:sameAs are known to violate the formal Leibniz-style semantics. We provide a more flexible semantics to identity by assigning meaning to the subrelations of an identity relation in terms of the predicates that are used in a knowledge-base. Using those indiscernability-predicates, we define upper and lower approximations of equality in the style of rought-set theory, resulting in a quality-measure for identity relations.",
  author    = "Wouter Beek and Stefan Schlobach and {van Harmelen}, Frank",
  year      = "2014",
  pages     = "587--589",
  booktitle = "14th International Conference on the Principles of Knowledge Representation and Reasoning, KR 2014",
  publisher = "AAAI Press",
}

Identity relations are at the foundation of many logic-based knowledge representations. We argue that the traditional notion of equality, is unsuited for many realistic knowledge representation settings. The classical interpretation of equality is too strong when the equality statements are re-used outside their original context. On the Semantic Web, equality statements are used to interlink multiple descriptions of the same object, using owl:sameAs assertions. And indeed, many practical uses of owl:sameAs are known to violate the formal Leibniz-style semantics. We provide a more flexible semantics to identity by assigning meaning to the subrelations of an identity relation in terms of the predicates that are used in a knowledge-base. Using those indiscernability-predicates, we define upper and lower approximations of equality in the style of rought-set theory, resulting in a quality-measure for identity relations.

@inbook{193c4d80ef3242c096790fea37f8b36f,
  title     = "Scientific Lenses to Support Multiple Views over Linked Chemistry Data",
  author    = "C. Batchelor and C Brenninkmeijer and C. Chichester and M. Davies and D. Digles and I. Dunlop and C.T. Evelo and A. Gaulton and C.A. Goble and A.J.G Gray and P.T. Groth and L Harland and K. Karapetyan and A. Loizou and J.P. Overington and S. Pettifer and J. Steele and R. Stevens and V. Tkachenko and A Waagmeester and A.J. Williams and E.L Willighagen",
  year      = "2014",
  booktitle = "The Semantic Web–ISWC 2014",
}

@article{1165c9b0439c4faa87efa6098120a44a,
  title     = "Semantic Representation of Evidence-based Clinical Guidelines",
  author    = "Z. Huang and {ten Teije}, A.C.M. and {van Harmelen}, F.A.H. and S. Ait-Mokhtar",
  note      = "Proceedings title: 6th International Workshop on Knowledge Representation for Health Care (KR4HC2014) Publisher: Springer ISBN: 978-3-319-13281-5 Editors: S. Miksch, D. Riano",
  year      = "2014",
  volume    = "8903",
  journal   = "Lecture Notes in Computer Science",
  issn      = "0302-9743",
  publisher = "Springer Verlag",
}

@article{6570c360d0544bbdb575308926eb82cf,
  title    = "Semantic Representation of Evidence-based Clinical Guidelines",
  abstract = "Evidence-based Clinical Guidelines (EbCGs) are that the document or recommendation has been created using the best clinical research findings of the highest value to aid in the delivery of optimum clinical care to patients. In this paper, we propose a lightweight formal-ism of evidence-based clinical guidelines by introducing the Semantic Web Technology for it. With the help of the tools which have been de-veloped in the Semantic Web and Natural Language Processing (NLP), the generation of the formulations of evidence-based clinical guidelines become much easy. We will discuss several use cases of the semantic representation of EbCGs, and argue that it is potentially useful for the applications of the semantic web technology on the medical domain.",
  author   = "Zhisheng Huang and Harmelen, {Frank Van}",
  year     = "2014",
  doi      = "10.1007/978-3-319-13281-5_6",
  pages    = "78--94",
  journal  = "6th International Workshop on Knowledge Representation for Health Care {KR4HC}",
}

Evidence-based Clinical Guidelines (EbCGs) are that the document or recommendation has been created using the best clinical research findings of the highest value to aid in the delivery of optimum clinical care to patients. In this paper, we propose a lightweight formal-ism of evidence-based clinical guidelines by introducing the Semantic Web Technology for it. With the help of the tools which have been de-veloped in the Semantic Web and Natural Language Processing (NLP), the generation of the formulations of evidence-based clinical guidelines become much easy. We will discuss several use cases of the semantic representation of EbCGs, and argue that it is potentially useful for the applications of the semantic web technology on the medical domain.

@inbook{a81fff84e674429290be451efa2fe20a,
  title     = "Simplifying RDF Data for Graph-Based Machine Learning.",
  abstract  = "From the perspective of machine learning and data mining applications, expressing data in RDF rather than a domain-specific for- mat can add complexity and obfuscate the internal structure. We in- vestigate and illustrate this issue with an example where bio-molecular graph datasets are expressed in RDF. We use this example to inspire pre- processing techniques which reverse some of the complications of adding semantic annotations, exposing those patterns in the data that are most relevant to machine learning. We test these methods in a number of clas- sification experiments and show that they can improve performance both for our example datasets and real-world RDF datasets.",
  author    = "P. Bloem and A. Wibisono and {de Vries}, G.K.D",
  year      = "2014",
  booktitle = "KNOW@ LOD",
}

From the perspective of machine learning and data mining applications, expressing data in RDF rather than a domain-specific for- mat can add complexity and obfuscate the internal structure. We in- vestigate and illustrate this issue with an example where bio-molecular graph datasets are expressed in RDF. We use this example to inspire pre- processing techniques which reverse some of the complications of adding semantic annotations, exposing those patterns in the data that are most relevant to machine learning. We test these methods in a number of clas- sification experiments and show that they can improve performance both for our example datasets and real-world RDF datasets.

@inbook{e309b7355486497ebd0952e62aff3230,
  title     = "Spatial Reasoning based Research of Road Network Intersection Patterns in Cities",
  author    = "D. Xu and Z. Huang and X. Zhang and D. Wang and Jiangli Zhang",
  year      = "2014",
  series    = "1",
  pages     = "31--35",
  booktitle = "Journal of Road Traffice and Safety",
}

@inbook{1a6e58e9007344c59c174b6547484d8f,
  title     = "Stay Point Identification of Mobile Phone Trajectory",
  author    = "R. Du and J. Huang and N. Zhong and Z. Huang",
  year      = "2014",
  series    = "8",
  pages     = "200--206",
  booktitle = "Journal of Frontiers of Computer Science and Technology,",
}

@article{29462648d9424241ae9f3c6d4eb31e12,
  title     = "Streaming the Web: Reasoning over dynamic data",
  abstract  = "In the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed and updated, and new data is continuously generated from a huge number of sources, often at high rate. In other words, fresh information is constantly made available in the form of streams of new data and updates. Despite some promising investigations in the area, stream reasoning is still in its infancy, both from the perspective of models and theories development, and from the perspective of systems and tools design and implementation. The aim of this paper is threefold: (i) we identify the requirements coming from different application scenarios, and we isolate the problems they pose; (ii) we survey existing approaches and proposals in the area of stream reasoning, highlighting their strengths and limitations; (iii) we draw a research agenda to guide the future research and development of stream reasoning. In doing so, we also analyze related research fields to extract algorithms, models, techniques, and solutions that could be useful in the area of stream reasoning. © 2014 Elsevier B.V. All rights reserved.",
  keywords  = "Complex Event Processing, Semantic Web, Stream processing, Stream reasoning, Survey",
  author    = "Alessandro Margara and Jacopo Urbani and {Van Harmelen}, Frank and Henri Bal",
  year      = "2014",
  doi       = "10.1016/j.websem.2014.02.001",
  volume    = "25",
  pages     = "24--44",
  journal   = "Journal of Web Semantics",
  issn      = "1570-8268",
  publisher = "Elsevier",
}

In the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed and updated, and new data is continuously generated from a huge number of sources, often at high rate. In other words, fresh information is constantly made available in the form of streams of new data and updates. Despite some promising investigations in the area, stream reasoning is still in its infancy, both from the perspective of models and theories development, and from the perspective of systems and tools design and implementation. The aim of this paper is threefold: (i) we identify the requirements coming from different application scenarios, and we isolate the problems they pose; (ii) we survey existing approaches and proposals in the area of stream reasoning, highlighting their strengths and limitations; (iii) we draw a research agenda to guide the future research and development of stream reasoning. In doing so, we also analyze related research fields to extract algorithms, models, techniques, and solutions that could be useful in the area of stream reasoning. © 2014 Elsevier B.V. All rights reserved.

@misc{5f6168c39c024fbbb6af3a83c8c55cb2,
  title  = "Structural Properties as Proxy for Semantic Relevance in RDF Graph Sampling",
  author = "L.J. Rietveld and R.J. Hoekstra and K.S. Schlobach and C.D.M. Gueret",
  year   = "2014",
}

@misc{8bb5f0a8c46b420aa45deb4b4228781d,
  title   = "Supporting trial recruitment and design by automatically interpreting eligibility criteria",
  author  = "K. Milian",
  note    = "Naam instelling promotie: VU Vrije Universiteit Naam instelling onderzoek: VU Vrije Universiteit",
  year    = "2014",
  school  = "Vrije Universiteit Amsterdam",
}

@inbook{fd6fc04a6c44499fa0a62ec0fe44c350,
  title     = "Towards a Conceptual Model for Enhancing Reasoning about Clinical Guidelines: A case-study on Comorbidity",
  abstract  = "Computer-Interpretable Guidelines (CIGs) are representations of Clinical Guidelines (CGs) in computer interpretable languages. CIGs have been pointed as an alternative to deal with the various limitations of paper based CGs to support healthcare activities. Although the improvements offered by existing CIG languages, the complexity of the medical domain requires advanced features in order to reuse, share, update, combine or personalize their contents. We propose a conceptual model for representing the content of CGs as a result from an iterative approach that take into account the content of real CGs, CIGs languages and foundational ontologies in order to enhance the reasoning capabilities required to address CIG use-cases. In particular, we apply our approach to the comorbidity use-case and illustrate the model with a realistic case study (Duodenal Ulcer and Transient Ischemic Attack) and compare the results against an existing approach.",
  author    = "{Carretta Zamborlini}, Veruska and {Da Silveira}, Marcos and Cedric Pruski and {ten Teije}, Annette and {van Harmelen}, Frank",
  note      = "Proceedings title: Proceedings of 6th International Workshop Knowledge Representation for Health-Care (KR4HC). Publisher: Springer Editors: S. Miksch, D. Riano, A. ten Teije",
  year      = "2014",
  doi       = "10.1007/978-3-319-13281-5_3",
  volume    = "8903",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "29--44",
  booktitle = "Knowledge Representation for Health Care - 6th International Workshop, KR4HC 2014, Revised Selected Papers",
}

Computer-Interpretable Guidelines (CIGs) are representations of Clinical Guidelines (CGs) in computer interpretable languages. CIGs have been pointed as an alternative to deal with the various limitations of paper based CGs to support healthcare activities. Although the improvements offered by existing CIG languages, the complexity of the medical domain requires advanced features in order to reuse, share, update, combine or personalize their contents. We propose a conceptual model for representing the content of CGs as a result from an iterative approach that take into account the content of real CGs, CIGs languages and foundational ontologies in order to enhance the reasoning capabilities required to address CIG use-cases. In particular, we apply our approach to the comorbidity use-case and illustrate the model with a realistic case study (Duodenal Ulcer and Transient Ischemic Attack) and compare the results against an existing approach.

@inbook{6d394da2e5c941c1990410c63950fa78,
  title     = "Using Semantic Web Technologies to Reproduce a Pharmacovigelance Case Study",
  author    = "M. Hildebrand and R.J. Hoekstra and {van Ossenbruggen}, J.R.",
  year      = "2014",
  editor    = "T Kauppinen and J Zhao and P.T. Groth and K. Kessler and L.C. Pouchard and C. Goble and Y. Gil and {van Erp}, M. and {van Ossenbruggen}, J.",
  booktitle = "3rd International Workshop on Linked Science 2013—Supporting Reproducibility, Scientific Investigations and Experiments (LISC2013)",
}

@inbook{9d75affb77e9494db1e5d55b95015c35,
  title     = "Validity Checking of Road Signs based on LarKC",
  author    = "D. Wang and Z. Huang and Q. Liu and X. Zhang and D. Xu",
  year      = "2014",
  pages     = "18--24",
  booktitle = "Journal of Road Traffic and Safety (ISSN 1008-2522)",
}

@book{29feaf635c0b476c8d1f0ff503fbeaeb,
  title     = "Web Information Systems Engineering - WISE 2013 Workshops",
  author    = "Z. Huang and Chenfei Liu and Jing He and Guangyan Huang",
  year      = "2014",
  series    = "1",
  publisher = "Springer",
  number    = "LNCS8182",
}

@inbook{1a922735da824990b12698b8ac46649b,
  title     = "WebQR: Building a Knowledge Representation Application on the Semantic Web",
  author    = "W.G.J. Beek and S. Latour and K.S. Schlobach",
  year      = "2014",
  pages     = "102--107",
  booktitle = "Proceedings of the ISWC Developers Workshop 2014",
}

@inbook{70e1f9ce1c6640c482c65181c2cad722,
  title     = "What is Linked Historical Data",
  author    = "A. Merono and R.J. Hoekstra",
  year      = "2014",
  editor    = "S. Schlobach and K. Janowicz",
  booktitle = "Proceedings of the 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2014)",
  publisher = "Springer",
}

@inbook{e751cac771d04b3188bafa82120e2953,
  title     = "YASGUI: Feeling the Pulse of Linked Data",
  author    = "L.J. Rietveld and R.J. Hoekstra",
  year      = "2014",
  booktitle = "Proceedings of the 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2014)",
}

@article{adb2dd43a92a42b28864166631fb7a95,
  title     = "Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks",
  abstract  = "OBJECTIVE: This paper describes a methodology which enables computer-aided support for the planning, visualization and execution of personalized patient treatments in a specific healthcare process, taking into account complex temporal constraints and the allocation of institutional resources. To this end, a translation from a time-annotated computer-interpretable guideline (CIG) model of a clinical protocol into a temporal hierarchical task network (HTN) planning domain is presented.MATERIALS AND METHODS: The proposed method uses a knowledge-driven reasoning process to translate knowledge previously described in a CIG into a corresponding HTN Planning and Scheduling domain, taking advantage of HTNs known ability to (i) dynamically cope with temporal and resource constraints, and (ii) automatically generate customized plans. The proposed method, focusing on the representation of temporal knowledge and based on the identification of workflow and temporal patterns in a CIG, makes it possible to automatically generate time-annotated and resource-based care pathways tailored to the needs of any possible patient profile.RESULTS: The proposed translation is illustrated through a case study based on a 70 pages long clinical protocol to manage Hodgkin's disease, developed by the Spanish Society of Pediatric Oncology. We show that an HTN planning domain can be generated from the corresponding specification of the protocol in the Asbru language, providing a running example of this translation. Furthermore, the correctness of the translation is checked and also the management of ten different types of temporal patterns represented in the protocol. By interpreting the automatically generated domain with a state-of-art HTN planner, a time-annotated care pathway is automatically obtained, customized for the patient's and institutional needs. The generated care pathway can then be used by clinicians to plan and manage the patients long-term care.CONCLUSION: The described methodology makes it possible to automatically generate patient-tailored care pathways, leveraging an incremental knowledge-driven engineering process that starts from the expert knowledge of medical professionals. The presented approach makes the most of the strengths inherent in both CIG languages and HTN planning and scheduling techniques: for the former, knowledge acquisition and representation of the original clinical protocol, and for the latter, knowledge reasoning capabilities and an ability to deal with complex temporal and resource constraints. Moreover, the proposed approach provides immediate access to technologies such as business process management (BPM) tools, which are increasingly being used to support healthcare processes.",
  keywords  = "Antineoplastic Protocols, Artificial Intelligence, Critical Pathways, Decision Making, Computer-Assisted, Hodgkin Disease, Humans, Long-Term Care, Patient Care Planning, Pediatrics, Practice Guidelines as Topic, Workflow, Journal Article, Research Support, Non-U.S. Gov't",
  author    = "A. González-Ferrer and {ten Teije}, A.C.M. and J. Fdez-Olivares and K. Milian",
  note      = "Copyright © 2012 Elsevier B.V. All rights reserved.",
  year      = "2013",
  month     = "2",
  doi       = "10.1016/j.artmed.2012.08.008",
  volume    = "57",
  pages     = "91--109",
  journal   = "Artificial Intelligence in Medicine",
  issn      = "0933-3657",
  publisher = "Elsevier",
  number    = "2",
}

OBJECTIVE: This paper describes a methodology which enables computer-aided support for the planning, visualization and execution of personalized patient treatments in a specific healthcare process, taking into account complex temporal constraints and the allocation of institutional resources. To this end, a translation from a time-annotated computer-interpretable guideline (CIG) model of a clinical protocol into a temporal hierarchical task network (HTN) planning domain is presented.MATERIALS AND METHODS: The proposed method uses a knowledge-driven reasoning process to translate knowledge previously described in a CIG into a corresponding HTN Planning and Scheduling domain, taking advantage of HTNs known ability to (i) dynamically cope with temporal and resource constraints, and (ii) automatically generate customized plans. The proposed method, focusing on the representation of temporal knowledge and based on the identification of workflow and temporal patterns in a CIG, makes it possible to automatically generate time-annotated and resource-based care pathways tailored to the needs of any possible patient profile.RESULTS: The proposed translation is illustrated through a case study based on a 70 pages long clinical protocol to manage Hodgkin's disease, developed by the Spanish Society of Pediatric Oncology. We show that an HTN planning domain can be generated from the corresponding specification of the protocol in the Asbru language, providing a running example of this translation. Furthermore, the correctness of the translation is checked and also the management of ten different types of temporal patterns represented in the protocol. By interpreting the automatically generated domain with a state-of-art HTN planner, a time-annotated care pathway is automatically obtained, customized for the patient's and institutional needs. The generated care pathway can then be used by clinicians to plan and manage the patients long-term care.CONCLUSION: The described methodology makes it possible to automatically generate patient-tailored care pathways, leveraging an incremental knowledge-driven engineering process that starts from the expert knowledge of medical professionals. The presented approach makes the most of the strengths inherent in both CIG languages and HTN planning and scheduling techniques: for the former, knowledge acquisition and representation of the original clinical protocol, and for the latter, knowledge reasoning capabilities and an ability to deal with complex temporal and resource constraints. Moreover, the proposed approach provides immediate access to technologies such as business process management (BPM) tools, which are increasingly being used to support healthcare processes.

@inbook{07e286a48adc4ca2874c9d2568251155,
  title     = "A Monitoring System for the Safety of Building Structure Based on Semantic Technology",
  author    = "H. Wang and Z. Huang and N. Zhong",
  year      = "2013",
  booktitle = "the IEEE-CPS Proceedings of the 2013 International Symposium on Intelligent Building and Buliding Automation (ISBBA2013)",
}

@inbook{1bf777bcc6b84cceb91534c5e3bb7c21,
  title     = "Application of Semantic Technology in Rational Use of Antibacterial Agents",
  author    = "S. Yu and L. Xu and Z. Huang",
  year      = "2013",
  booktitle = "Proceedings of the 8th International Conference on Cooperation and Promotion of Information Resources in Science and Technology (COINFO 2013)",
  publisher = "China Science Press",
}

@inbook{a8c8c60fa6e84ca1bcbc1a8685d21fab,
  title     = "A Practical Query Language for Graph DBs",
  author    = "{Angles Rojas}, R. and P. Barcelo and G. Rios",
  year      = "2013",
  booktitle = "Alberto Mendelzon International Workshop on Foundations of Data Management",
}

@misc{fb48e56abc7544fcb0e24a476fbe8012,
  title  = "A Semantically-Enabled System for Clinical Trials",
  author = "Z. Huang and {ten Teije}, A.C.M. and {van Harmelen}, F.A.H.",
  note   = "Proceedings title: 25th Benelux Conference on Artificial Intelligence Editors: K Hindriks, M de Weerdt, B van Riemsdijk, M Warnier",
  year   = "2013",
}

@inbook{3e982bf976e64511ada5e07ef08fa0c1,
  title     = "A Summary of the Workshop and Tutorial Program at ESWC 2013",
  author    = "J. Voelker and K.S. Schlobach",
  year      = "2013",
  booktitle = "The Semantic Web: ESWC 2013 Satellite Events",
}

@inbook{7ef24e6c9ca7439491e093b85893a3d9,
  title     = "Automatic Schema Design For Co-Clustered Tables",
  author    = "S. Baumann and P.A. Boncz and K.-U. Sattler",
  year      = "2013",
  booktitle = "Proceedings of International Workshop on Self-Managing Database Systems 2013 (SMDB )",
}

@article{87b9253386bb402d9f48f4a46df2e4dd,
  title     = "Barriers to the reuse of routinely recorded clinical data: a field report",
  abstract  = "Today, clinical data is routinely recorded in vast amounts, but its reuse can be challenging. A secondary use that should ideally be based on previously collected clinical data is the computation of clinical quality indicators. In the present study, we attempted to retrieve all data from our hospital that is required to compute a set of quality indicators in the domain of colorectal cancer surgery. We categorised the barriers that we encountered in the scope of this project according to an existing framework, and provide recommendations on how to prevent or surmount these barriers. Assuming that our case is not unique, these recommendations might be applicable for the design, evaluation and optimisation of Electronic Health Records.",
  keywords  = "Attitude of Health Personnel, Colorectal Neoplasms, Computer Literacy, Data Mining, Electronic Health Records, Humans, Medical Record Linkage, Netherlands, Prevalence, Journal Article",
  author    = "Kathrin Dentler and {ten Teije}, Annette and {De Keizer}, {Nicolette F.} and Ronald Cornet",
  year      = "2013",
  volume    = "192",
  pages     = "313--7",
  journal   = "Studies in Health Technology and Informatics",
  issn      = "0926-9630",
  publisher = "IOS Press",
}

Today, clinical data is routinely recorded in vast amounts, but its reuse can be challenging. A secondary use that should ideally be based on previously collected clinical data is the computation of clinical quality indicators. In the present study, we attempted to retrieve all data from our hospital that is required to compute a set of quality indicators in the domain of colorectal cancer surgery. We categorised the barriers that we encountered in the scope of this project according to an existing framework, and provide recommendations on how to prevent or surmount these barriers. Assuming that our case is not unique, these recommendations might be applicable for the design, evaluation and optimisation of Electronic Health Records.

@inbook{504cdc95cc514923848b587de36d3ae3,
  title     = "Barriers to the Reuse of Routinely Recorded Clinical Data: A Field Report",
  abstract  = "Today, clinical data is routinely recorded in vast amounts, but its reuse can be challenging. A secondary use that should ideally be based on previously collected clinical data is the computation of clinical quality indicators. In the present study, we attempted to retrieve all data from our hospital that is required to compute a set of quality indicators in the domain of colorectal cancer surgery. We categorised the barriers that we encountered in the scope of this project according to an existing framework, and provide recommendations on how to prevent or surmount these barriers. Assuming that our case is not unique, these recommendations might be applicable for the design, evaluation and optimisation of Electronic Health Records. © 2013 IMIA and IOS Press.",
  author    = "K. Dentler and {ten Teije}, A. and {De Keizer}, N. and R. Cornet",
  year      = "2013",
  doi       = "10.3233/978-1-61499-289-9-313",
  isbn      = "9781614992899",
  pages     = "313--317",
  editor    = "Lehmann, {C. U.}",
  booktitle = "MEDINFO 2013",
  publisher = "IOS",
}

Today, clinical data is routinely recorded in vast amounts, but its reuse can be challenging. A secondary use that should ideally be based on previously collected clinical data is the computation of clinical quality indicators. In the present study, we attempted to retrieve all data from our hospital that is required to compute a set of quality indicators in the domain of colorectal cancer surgery. We categorised the barriers that we encountered in the scope of this project according to an existing framework, and provide recommendations on how to prevent or surmount these barriers. Assuming that our case is not unique, these recommendations might be applicable for the design, evaluation and optimisation of Electronic Health Records. © 2013 IMIA and IOS Press.

@article{38c97fb87cdd44e5a169505f30375aa7,
  title     = "Barriers to the Reuse of Routinely Recorded Clinical Data: A Field Report",
  abstract  = "Today, clinical data is routinely recorded in vast amounts, but its reuse can be challenging. A secondary use that should ideally be based on previously collected clinical data is the computation of clinical quality indicators. In the present study, we attempted to retrieve all data from our hospital that is required to compute a set of quality indicators in the domain of colorectal cancer surgery. We categorised the barriers that we encountered in the scope of this project according to an existing framework, and provide recommendations on how to prevent or surmount these barriers. Assuming that our case is not unique, these recommendations might be applicable for the design, evaluation and optimisation of Electronic Health Records. © 2013 IMIA and IOS Press.",
  author    = "K. Dentler and {ten Teije}, A.C.M. and {de Keizer}, N.F. and R. Cornet",
  note      = "Proceedings title: Medinfo 2013 Publisher: Imia and IOS press",
  year      = "2013",
  doi       = "10.3233/978-1-61499-289-9-313",
  volume    = "192",
  journal   = "Studies in Health Technology and Informatics",
  issn      = "0926-9630",
  publisher = "IOS Press",
}

Today, clinical data is routinely recorded in vast amounts, but its reuse can be challenging. A secondary use that should ideally be based on previously collected clinical data is the computation of clinical quality indicators. In the present study, we attempted to retrieve all data from our hospital that is required to compute a set of quality indicators in the domain of colorectal cancer surgery. We categorised the barriers that we encountered in the scope of this project according to an existing framework, and provide recommendations on how to prevent or surmount these barriers. Assuming that our case is not unique, these recommendations might be applicable for the design, evaluation and optimisation of Electronic Health Records. © 2013 IMIA and IOS Press.

@inbook{b4b72d4b0e82496cb7d09ccf8044ae94,
  title     = "Benchmarking database systems for social network applications",
  author    = "{Angles Rojas}, R. and A. Prat-Pérez and D. Dominguez-Sal and Josep-Lluis Larriba-Pey",
  year      = "2013",
  booktitle = "Graph Data-management Experiences & Systems (GRADES)",
}

@article{83a2e1502b3840fc8556226509352170,
  title     = "Conceptual and Computational Analysis of the Role of Emotions and Social Influence in Learning",
  author    = "J. Treur and {van Wissen}, A.",
  note      = "Proceedings title: Proceedings of the Third World Conference on Learning, Teaching and Educational Leadership Publisher: Procedia Social and Behavioral Sciences, Elsevier",
  year      = "2013",
  doi       = "10.1016/j.sbspro.2013.09.220",
  volume    = "93",
  pages     = "449--467",
  journal   = "Procedia Social and Behavioral Sciences",
  issn      = "1877-0428",
  publisher = "Elsevier BV",
}

@inbook{11d7ed31bd004cd7823f7a011e56354b,
  title     = "DataHives: Triple Store Enhancement Using A Bee Calculus",
  abstract  = "The Linked Open Data (LOD) cloud is too big for efficient computation and too heterogeneous for standard materialization techniques to cope with. The purpose of the DataHives system is to solve both of these problems by utilizing swarm intelligence to enhance a curated dataset. The system spawns software agentsthat traverse the LOD cloud looking for extensions to the curated dataset that are relevant and trusted.",
  author    = "Pepijn Kroes and Wouter Beek and Stefan Schlobach",
  year      = "2013",
  pages     = "380--381",
  editor    = "{Koen Hindriks}, K. and {de Weerdt}, M. and {van Riemsdijk}, B. and M. Warnier",
  booktitle = "Proceedings of the 25th Belgium-Netherlands Artificial Intelligence Conference",
}

The Linked Open Data (LOD) cloud is too big for efficient computation and too heterogeneous for standard materialization techniques to cope with. The purpose of the DataHives system is to solve both of these problems by utilizing swarm intelligence to enhance a curated dataset. The system spawns software agentsthat traverse the LOD cloud looking for extensions to the curated dataset that are relevant and trusted.

@inbook{3a9fa7d189134bf5842f7bd9a2ada835,
  title     = "Design and Implementation of Visualization Tools for Advanced Patient Data Generator",
  author    = "M. Zhang and Z. Huang and J. Gu",
  year      = "2013",
  booktitle = "Proceedings of 2013 International Workshop on Semantic Technology for eHealth (STeH2013)",
  publisher = "Springer",
}

@inbook{f3e837848031463dbc1cbd01d38ec25f,
  title     = "Detecting and Reporting Extensional Concept Drift in Statistical Linked Data",
  author    = "A. Merono and C.D.M. Gueret and R.J. Hoekstra and K.S. Schlobach",
  year      = "2013",
  booktitle = "Proceedings of the 1st International Workshop on Semantic Statistics (SemStats 2013), ISWC 2013",
  publisher = "CEUR",
}

@inbook{a038c99802254d429e5a77f38854da24,
  title     = "Developing a Brain Informatics Provenance Model",
  author    = "H. Zhong and J. Chen and J. Han and T. Kotake and N. Zhong and Z. Huang",
  year      = "2013",
  booktitle = "Proceedings of 2013 International Conference on Brain and Health Informatics, Special Session on Special Session on Human Centered Computing",
  publisher = "Springer",
}

@inbook{520b0a6a5ea64421bcc47926aa0b74ec,
  title     = "DynaLearn - An Intelligent Learning Environment for Learning Conceptual Knowledge",
  abstract  = "Articulating thought in computer-based media is a powerful means for\nhumans to develop their understanding of phenomena. We have created\nDynaLearn, an intelligent learning environment that allows learners to\nacquire conceptual knowledge by constructing and simulating qualitative\nmodels of how systems behave. DynaLearn uses diagrammatic\nrepresentations for learners to express their ideas. The environment is\nequipped with semantic technology components that are capable of\ngenerating knowledge-based feedback and virtual characters that enhance\nthe interaction with learners. Teachers have created course material,\nand successful evaluation studies have been performed. This article\npresents an overview of the DynaLearn system.",
  author    = "Bert Bredeweg and Jochem Liem and Wouter Beek and Floris Linnebank and Jorge Gracia and Esther Lozano and Michael Wissner and Rene Buehling and Paulo Salles and Richard Noble and Andreas Zitek and Petya Borisova and David Mioduser",
  year      = "2013",
  isbn      = "0738-4602",
  series    = "AI MAGAZINE",
  pages     = "46--65",
  booktitle = "AI MAGAZINE",
}

Articulating thought in computer-based media is a powerful means for\nhumans to develop their understanding of phenomena. We have created\nDynaLearn, an intelligent learning environment that allows learners to\nacquire conceptual knowledge by constructing and simulating qualitative\nmodels of how systems behave. DynaLearn uses diagrammatic\nrepresentations for learners to express their ideas. The environment is\nequipped with semantic technology components that are capable ofŋenerating knowledge-based feedback and virtual characters that enhance\nthe interaction with learners. Teachers have created course material,\nand successful evaluation studies have been performed. This article\npresents an overview of the DynaLearn system.

@article{900acaf85bd542f4bb17012e5751a16f,
  title     = "DynaLearn-An Intelligent Learning Environment for Learning Conceptual Knowledge",
  abstract  = "Articulating thought in computerbased media is a powerful means for humans to develop their understanding of phenomena. We have created DynaLearn, an intelligent learning environment that allows learners to acquire conceptual knowledge by constructing and simulating qualitative models of how systems behave. DynaLearn uses diagrammatic representations for learners to express their ideas. The environment is equipped with semantic technology components that are capable of generating knowledge- based feedback and virtual characters that enhance the interaction with learners. Teachers have created course material, and successful evaluation studies have been performed. This article presents an overview of the DynaLearn system. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.",
  author    = "Bert Bredeweg and Jochem Liem and Wouter Beek and Floris Linnebank and Jorge Gracia and Esther Lozano and Michael Wißner and René Bühling and Paulo Salles and Richard Noble and Andreas Zitek and Petya Borisova and David Mioduser",
  year      = "2013",
  volume    = "34",
  pages     = "46--65",
  journal   = "The AI Magazine",
  issn      = "0738-4602",
  publisher = "AI Access Foundation",
  number    = "4",
}

Articulating thought in computerbased media is a powerful means for humans to develop their understanding of phenomena. We have created DynaLearn, an intelligent learning environment that allows learners to acquire conceptual knowledge by constructing and simulating qualitative models of how systems behave. DynaLearn uses diagrammatic representations for learners to express their ideas. The environment is equipped with semantic technology components that are capable of generating knowledge- based feedback and virtual characters that enhance the interaction with learners. Teachers have created course material, and successful evaluation studies have been performed. This article presents an overview of the DynaLearn system. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

@inbook{3160586e9d6846b9a8daa224e7d927e4,
  title     = "DynamiTE: Parallel Materialization of Dynamic RDF Data",
  author    = "J. Urbani and A. Margara and C.J.H. Jacobs and {van Harmelen}, F.A.H. and H.E. Bal",
  year      = "2013",
  pages     = "657--672",
  booktitle = "12th Int. Semantic Web Conference (ISWC 2013)",
  publisher = "Springer",
}

@inbook{3ad79a7f3a1440a4bc7e39cd22b55b99,
  title     = "Experiences With Virtuoso Cluster RDF Column Store",
  author    = "P.A. Boncz and O. Erling and {Minh Duc}, P.",
  year      = "2013",
  booktitle = "Linked Data Management: Principles and Techniques",
  publisher = "CRC Press",
}

@article{ce8b4c51717a46308eab9aa847a873c6,
  title     = "Formalization and computation of quality measures based on electronic medical records",
  abstract  = "Objective: Ambiguous definitions of quality measures in natural language impede their automated computability and also the reproducibility, validity, timeliness, traceability, comparability, and interpretability of computed results. Therefore, quality measures should be formalized before their release. We have previously developed and successfully applied a method for clinical indicator formalization (CLIF). The objective of our present study is to test whether CLIF is generalizable- that is, applicable to a large set of heterogeneous measures of different types and from various domains. Materials and methods: We formalized the entire set of 159 Dutch quality measures for general practice, which contains structure, process, and outcome measures and covers seven domains. We relied on a web-based tool to facilitate the application of our method. Subsequently, we computed the measures on the basis of a large database of real patient data. Results: Our CLIF method enabled us to fully formalize 100% of the measures. Owing to missing functionality, the accompanying tool could support full formalization of only 86% of the quality measures into Structured Query Language (SQL) queries. The remaining 14% of the measures required manual application of our CLIF method by directly translating the respective criteria into SQL. The results obtained by computing the measures show a strong correlation with results computed independently by two other parties. Conclusions: The CLIF method covers all quality measures after having been extended by an additional step. Our web tool requires further refinement for CLIF to be applied completely automatically. We therefore conclude that CLIF is sufficiently generalizable to be able to formalize the entire set of Dutch quality measures for general practice.",
  author    = "K. Dentler and M. Numans and {ten Teije}, A. and R. Cornet and {De Keizer}, N.",
  year      = "2013",
  doi       = "10.1136/amiajnl-2013-001921",
  journal   = "Journal of the American Medical Informatics Association",
  issn      = "1067-5027",
  publisher = "Oxford University Press",
}

Objective: Ambiguous definitions of quality measures in natural language impede their automated computability and also the reproducibility, validity, timeliness, traceability, comparability, and interpretability of computed results. Therefore, quality measures should be formalized before their release. We have previously developed and successfully applied a method for clinical indicator formalization (CLIF). The objective of our present study is to test whether CLIF is generalizable- that is, applicable to a large set of heterogeneous measures of different types and from various domains. Materials and methods: We formalized the entire set of 159 Dutch quality measures for general practice, which contains structure, process, and outcome measures and covers seven domains. We relied on a web-based tool to facilitate the application of our method. Subsequently, we computed the measures on the basis of a large database of real patient data. Results: Our CLIF method enabled us to fully formalize 100% of the measures. Owing to missing functionality, the accompanying tool could support full formalization of only 86% of the quality measures into Structured Query Language (SQL) queries. The remaining 14% of the measures required manual application of our CLIF method by directly translating the respective criteria into SQL. The results obtained by computing the measures show a strong correlation with results computed independently by two other parties. Conclusions: The CLIF method covers all quality measures after having been extended by an additional step. Our web tool requires further refinement for CLIF to be applied completely automatically. We therefore conclude that CLIF is sufficiently generalizable to be able to formalize the entire set of Dutch quality measures for general practice.

@inbook{8764a7f95d8e46ca9410331e77bca45e,
  title     = "Generation of Semantic Data from Guidelines for Rational Use of Antibiotics",
  author    = "Q. Hu and Z. Huang and J. Gu",
  year      = "2013",
  booktitle = "Proceedings of 2013 International Workshop on Semantic Technology for eHealth (STeH2013)",
  publisher = "Springer",
}

@article{7131b396f2fe4f1cb4832082e4619c82,
  title     = "Hybrid reasoning on OWL RL",
  author    = "J. Urbani and R Piro and {van Harmelen}, F.A.H. and H.E. Bal",
  year      = "2013",
  journal   = "Semantic Web",
  issn      = "1570-0844",
  publisher = "IOS Press",
}

@inbook{bb18383308bf44539aa052e4b17a02b3,
  title     = "Identifying most relevant concepts to describe clinical trial eligibility criteria",
  author    = "K. Milian and A. Bucur and {van Harmelen}, F.A.H. and {ten Teije}, A.C.M.",
  year      = "2013",
  booktitle = "International Conference on Health Informatics (HEALTHINF 2013)",
}

@inbook{e9694ddfc6ff4d4ea071e344096fcf37,
  title     = "Identifying research talent using web-centric databases",
  author    = "A. Dumitrache and P.T. Groth and {van den Besselaar}, P.A.A.",
  note      = "Gebeurtenis: ACM Web Science Conference, Paris, May 2013",
  year      = "2013",
  isbn      = "9781450318891",
  pages     = "57--60",
  editor    = "H. Davis and H. Halpin and A. Pentland and M. Bernstein and L. Adamic and H. Alani and A. Monnin and R. Rogers",
  booktitle = "Web Science",
  publisher = "ACM",
}

@article{e18f608ad5c74b508b1e31aa1e6b77ae,
  title     = "Including Co-referent URIs in a SPARQL Query",
  author    = "C Brenninkmeijer and C.A. Goble and A.J.G Gray and P.T. Groth and A. Loizou and S Pettifier",
  note      = "Proceedings title: Fourth International Workshop on Consuming Linked Data, COLD 2013 Publisher: CEUR-WS.org",
  year      = "2013",
  volume    = "1034",
  journal   = "CEUR workshop proceedings",
  issn      = "1613-0073",
  publisher = "CEUR Workshop Proceedings",
}

@inbook{a5224b05ee004298bbd93786f2316a8d,
  title     = "Incorporating Commercial and Private Data into an Open Linked Data Platform for Drug Discovery",
  author    = "C.A. Goble and A.J.G Gray and L Harland and K. Karapetyan and A. Loizou and I. Mikhailov and Y. Rankka and S. Senger and V. Tkachenko and A.J. Williams and E.L Willighagen",
  year      = "2013",
  booktitle = "12th Int. Semantic Web Conference (ISWC 2013)",
  publisher = "Springer",
}

@article{36955f7c0d4542a6af6f6a62c57eef05,
  title     = "Integration of Pattern-Based Debugging Approach into RaDON",
  author    = "Q. Ji and Z. Gao and Z. Huang",
  note      = "Proceedings title: Proceedings of 7th China Semantic Web Symposium & 2nd Web Science Conference (CSWS 2013) Publisher: Springer",
  year      = "2013",
  volume    = "406",
  pages     = "243--246",
  journal   = "Communications in Computer and Information Science",
  issn      = "1865-0929",
  publisher = "Springer Verlag",
}

@inbook{087d273978204bc993073b8626165e33,
  title     = "Intelligent Monitoring on Use of Antibacterial Agents based on Semantic Technology",
  author    = "X. Hua and C. Chen and Z. Huang",
  year      = "2013",
  pages     = "12--15",
  booktitle = "Journal of China Digital Medicine, Vol.8, No.4",
}

@inbook{ad639dcaf4fd4c6f955c66b55b188bfc,
  title     = "Interface Design of Semantic System for Road Sign Managemen",
  author    = "N. Li and Z. Huang and D. Xu",
  year      = "2013",
  booktitle = "Proceedings of 2013 International Workshop on Semantic Technology for Smart City",
  publisher = "Springer",
}

@article{371b66c6e07845768e482114ed32c7ca,
  title     = "Knowledge-based Patient Data Generation",
  author    = "Z. Huang and {van Harmelen}, F.A.H. and {ten Teije}, A.C.M. and K. Dentler",
  note      = "Proceedings title: Process Support and Knowledge Representation in Health Care Publisher: Springer Editors: D Riano, R Lenz, S Miksch, M Peleg, M Reichert, A.C.M. ten Teije",
  year      = "2013",
  volume    = "8268",
  pages     = "11--25",
  journal   = "Lecture Notes in Computer Science",
  issn      = "0302-9743",
  publisher = "Springer Verlag",
}

@inbook{4db741f0dbc34ea68b003d1284249548,
  title     = "Linkitup: Link Discovery for Research Data",
  author    = "R.J. Hoekstra and P.T. Groth",
  year      = "2013",
  editor    = "G.A.P.C. Burns and Y. Gil and Y. Liu and N. Villanueva-Rosales",
  booktitle = "Proceedings of the AAAI Fall Symposium on Discovery Informatics",
  publisher = "AAAI",
}

@inbook{26d8ed12d1cd46fb913384699b55e5da,
  title     = "Longitudinal Queries over Linked Census Data",
  author    = "A. Merono and R.J. Hoekstra and A. Scharnhorst and C.D.M. Gueret and A. Ashkpour",
  year      = "2013",
  isbn      = "9783642382871",
  editor    = "P. Cimiano and O. Corcho and V. Presutti and L. Hollink and S. Rudolph",
  booktitle = "Proceedings of the 10th Extended Semantic Web Conference, ESWC 2013",
  publisher = "Springer-Verlag",
}

@article{0e159bf14aae49deb75a7a36761b1835,
  title     = "Making Web-Scale Semantic Reasoning More Service-Oriented: The Large Knowledge Collider,",
  author    = "A Cheptsov and Z. Huang",
  note      = "Proceedings title: Proceedings of the Web Information Systems Engineering - Combined WISE 2011 and WISE 2012 Workshops, 2013 Publisher: Springer",
  year      = "2013",
  volume    = "7652",
  pages     = "13--26",
  journal   = "Lecture Notes in Computer Science",
  issn      = "0302-9743",
  publisher = "Springer Verlag",
}

@inbook{f121e2947bc34d62a40a3dc42d3cea78,
  title     = "Micro Adaptivity In Vectorwise",
  author    = "B. Raducanu and M. Zukowski and P.A. Boncz",
  year      = "2013",
  booktitle = "Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD, 2013",
  publisher = "ACM Press",
}

@article{88ca81af963648ed876694828c0a28df,
  title     = "Modelling Collective Decision Making in Groups and Crowds: Integrating Social Contagion and Interacting Emotions, Beliefs and Intentions",
  abstract  = "Collective decision making involves on the one hand individual mental states such as beliefs, emotions and intentions, and on the other hand interaction with others with possibly different mental states. Achieving a satisfactory common group decision on which all agree requires that such mental states are adapted to each other by social interaction. Recent developments in social neuroscience have revealed neural mechanisms by which such mutual adaptation can be realised. These mechanisms not only enable intentions to converge to an emerging common decision, but at the same time enable to achieve shared underlying individual beliefs and emotions. This paper presents a computational model for such processes. As an application of the model, an agent-based analysis was made of patterns in crowd behaviour, in particular to simulate a real-life incident that took place on May 4, 2010 in Amsterdam. From available video material and witness reports, useful empirical data were extracted. Similar patterns were achieved in simulations, whereby some of the parameters of the model were tuned to the case addressed, and most parameters were assigned default values. The results show the inclusion of contagion of belief, emotion, and intention states of agents results in better reproduction of the incident than non-inclusion. © 2012 The Author(s).",
  author    = "T. Bosse and M. Hoogendoorn and M.C.A. Klein and J. Treur and {van der Wal}, C.N. and {van Wissen}, A.",
  year      = "2013",
  doi       = "10.1007/s10458-012-9201-1",
  volume    = "27",
  pages     = "52--84",
  journal   = "Autonomous Agents and Multi-Agent Systems",
  issn      = "1387-2532",
  publisher = "Springer Netherlands",
  number    = "1",
}

Collective decision making involves on the one hand individual mental states such as beliefs, emotions and intentions, and on the other hand interaction with others with possibly different mental states. Achieving a satisfactory common group decision on which all agree requires that such mental states are adapted to each other by social interaction. Recent developments in social neuroscience have revealed neural mechanisms by which such mutual adaptation can be realised. These mechanisms not only enable intentions to converge to an emerging common decision, but at the same time enable to achieve shared underlying individual beliefs and emotions. This paper presents a computational model for such processes. As an application of the model, an agent-based analysis was made of patterns in crowd behaviour, in particular to simulate a real-life incident that took place on May 4, 2010 in Amsterdam. From available video material and witness reports, useful empirical data were extracted. Similar patterns were achieved in simulations, whereby some of the parameters of the model were tuned to the case addressed, and most parameters were assigned default values. The results show the inclusion of contagion of belief, emotion, and intention states of agents results in better reproduction of the incident than non-inclusion. © 2012 The Author(s).

@inbook{d70fe1f961a3455c9a922c53e7324714,
  title     = "Non-Temporal Orderings as Proxies for Extensional Concept Drift",
  author    = "A. Merono and K.S. Schlobach",
  year      = "2013",
  booktitle = "Proceedings of the 1st International Workshop on Semantic Statistics (SemStats 2013), challenge paper",
  publisher = "CEUR",
}

@inbook{15adffb873db429d833b0d85d14b7f17,
  title     = "NoSQL Databases for RDF: An Empirical Evaluation",
  author    = "S. Fundatureanu and P. Groth and M. Wylot and P. Cudré-Mauroux and I. Enchev and A. Haque and A. Harth and Keppmann, {F. L.} and D. Miranker and J. Sequeda",
  year      = "2013",
  booktitle = "12th Int. Semantic Web Conference (ISWC 2013)",
}

@inbook{fc0f30d4203f446994401e27c80f8617,
  title     = "On the Generation System of Mobile Data Based on Real User Behavior",
  author    = "R. Du and J. Huang and Z. Huang",
  year      = "2013",
  booktitle = "Proceedings of 2013 International Workshop on Big Web Data",
  publisher = "Springer",
}

@article{f6f84cd9bbf1493c8562e83be5a2f1b6,
  title     = "Order matters! Harnessing a world of orderings for reasoning over massive data.",
  abstract  = "More and more applications require real-time processing of massive, dynamically generated, ordered data; order is an essential factor as it reflects recency or relevance. Semantic technologies risk being unable to meet the needs of such applications, as they are not equipped with the appropriate instruments for answering queries over massive, highly dynamic, ordered data sets. In this vision paper, we argue that some data management techniques should be exported to the context of semantic technologies, by integrating ordering with reasoning, and by using methods which are inspired by stream and rank-aware data management.We systematically explore the problem space, and point both to problems which have been successfully approached and to problems which still need fundamental research, in an attempt to stimulate and guide a paradigm shift in semantic technologies. © IOS Press and the authors. All rights reserved.",
  author    = "{Della Valle}, E and K.S. Schlobach and M. Krötzsch and A. Bozzon and S. Ceri and I. Horrocks",
  year      = "2013",
  doi       = "10.3233/SW-2012-0085",
  volume    = "4",
  pages     = "219--231",
  journal   = "Semantic Web",
  issn      = "1570-0844",
  publisher = "IOS Press",
  number    = "2",
}

More and more applications require real-time processing of massive, dynamically generated, ordered data; order is an essential factor as it reflects recency or relevance. Semantic technologies risk being unable to meet the needs of such applications, as they are not equipped with the appropriate instruments for answering queries over massive, highly dynamic, ordered data sets. In this vision paper, we argue that some data management techniques should be exported to the context of semantic technologies, by integrating ordering with reasoning, and by using methods which are inspired by stream and rank-aware data management.We systematically explore the problem space, and point both to problems which have been successfully approached and to problems which still need fundamental research, in an attempt to stimulate and guide a paradigm shift in semantic technologies. © IOS Press and the authors. All rights reserved.

@inbook{97994a87bf844a8faf322b96ca9f4808,
  title     = "Pharmaceutical semantic database query mechanism Based on Key words",
  author    = "Y. Sun and J. Gu and Z. Huang",
  year      = "2013",
  pages     = "199--206",
  booktitle = "Proceedings of 7th China Semantic Web Symposium & 2nd Web Science Conference (CSWS 2013)",
  publisher = "Springer",
}

@inbook{8024c5ea075949189a0073d10a974dcb,
  title     = "Port city road sign management system based on semantic technology",
  author    = "Q. Liu and Z. Huang",
  year      = "2013",
  booktitle = "Journal of Industrial Instrumentation & Automation",
  publisher = "3",
}

@inbook{1f7367512d3b4a308b8aa657c69b5aaf,
  title     = "Pragmatic Semantics for the Web of Data",
  author    = "K.S. Schlobach and {van Beek}, W.",
  year      = "2013",
  booktitle = "2013 AAAI Fall Symposium Series",
}

@article{7595431b768a42b6bf547f977b25ca6b,
  title     = "Preface to the Advance Reasoning Technology for eScience (ART-2012) Workshop",
  author    = "Z. Huang and A Cheptsov and J. Pan",
  note      = "Proceedings title: Proceedings of the Web Information Systems Engineering - Combined WISE 2011 and WISE 2012 Workshops, 2013 Publisher: Springer",
  year      = "2013",
  volume    = "7652",
  pages     = "1--1",
  journal   = "Lecture Notes in Computer Science",
  issn      = "0302-9743",
  publisher = "Springer Verlag",
}

@inbook{077adf66f46f4e71b5e7313557e4d413,
  title     = "Preliminary Construction of the Semantic Web Course",
  author    = "W. Hu and Y. Qu and Z. Huang",
  year      = "2013",
  pages     = "73--76",
  booktitle = "Journal of Computer Education, No.192(12)",
}

@book{fc39aa1538be40c6bf98dec9c9d5d5a3,
  title     = "Proceedings of the 2013 international workshop on web-scale knowledge representation, retrieval, and reasoning",
  author    = "Y. Zeng and S. Kotoulas and Z. Huang",
  year      = "2013",
  series    = "ACM International Conference Proceeding Series 2013",
  publisher = "ACM Press",
}

@book{7ca1ce14eb1f4ae09b7f730021a493fe,
  title     = "Proceedings of the 2013 international workshop on web-scale knowledge representation, retrieval, and reasoning",
  author    = "Y. Zeng and S. Kotoulas and Z. Huang",
  year      = "2013",
  series    = "ACM International Conference on Information and Knowledge Management",
  publisher = "ACM",
  number    = "22",
}

@book{cc4b89bb09e84e54aabf0ead6e0df179,
  title     = "Proceedings of the Web Information Systems Engineering - Combined WISE 2011 and WISE 2012 Workshops",
  author    = "A. Haller and G. Huang and Z. Huang and H. Paik and Q. Sheng",
  year      = "2013",
  isbn      = "9783642383328",
  series    = "LNCS",
  publisher = "Springer",
  number    = "7652",
}

@book{7959ba771c7543169bde997029a08d37,
  title     = "Process support and knowledge representation in health care",
  author    = "D Riano and R. Lenz and S Miksch and M Peleg and M. Reichert and {ten Teije}, A.C.M.",
  note      = "Gebeurtenis: KR4HC 2013/ProHealth 2013",
  year      = "2013",
  isbn      = "9783319039169",
  series    = "Lecture Notes CS",
  publisher = "Springer",
  number    = "8268",
}

@book{6d058c870939444cbb3e16b4489150dc,
  title     = "Process Support and Knowledge Representation in Health Care",
  author    = "R. Lenz and S Miksch and M Peleg and M. Reichert and D Riano and {ten Teije}, A.C.M.",
  year      = "2013",
  isbn      = "9783642364372",
  series    = "Lecture Notes in Computer Science",
  publisher = "Springer Berlin Heidelberg",
  number    = "7738",
}

@misc{fb9b94c634a94a3e86c09f8fbf31cbd6,
  title   = "Reasoning with Contexts in Description Logics",
  author  = "S. Klarman",
  note    = "Naam instelling promotie: VU Vrije Universiteit Naam instelling onderzoek: VU Vrije Universiteit",
  year    = "2013",
  school  = "Vrije Universiteit Amsterdam",
}

@inbook{02ea4f9400e8437aa29e69d9632eb044,
  title     = "Recycling In Pipelined Query Evaluation",
  author    = "F. Nagel and P.A. Boncz and S. Viglas",
  year      = "2013",
  booktitle = "Proceedings of the IEEE International Conference on Data Engineering (ICDE, 2013)",
  publisher = "IEEE",
}

@inbook{596fae6ddd35499db6748d9a0ca4da69,
  title     = "Redundant Elements in SNOMED CT Concept Definitions",
  author    = "K. Dentler and R. Cornet",
  year      = "2013",
  isbn      = "9783642383267",
  series    = "2013",
  publisher = "Springer",
  pages     = "186--195",
  editor    = "N. Peek and Morales, {R. M.} and M. Peleg",
  booktitle = "Artificial Intelligence in Medicine",
}

@article{c453ec4f76da4737bcf9e9eeeb83521f,
  title     = "Repurposing Benchmark Corpora for Reconstructing Provenance",
  author    = "S. Magliacane and P.T. Groth",
  note      = "Proceedings title: 3rd Workshop on Semantic Publishing Publisher: CEUR-WS.org",
  year      = "2013",
  volume    = "994",
  journal   = "CEUR workshop proceedings",
  issn      = "1613-0073",
  publisher = "CEUR Workshop Proceedings",
}

@inbook{f88d8999f1f546cb8b262df18493ba40,
  title     = "Results of the Ontology Alignment Evaluation Initiative 2012",
  author    = "J.L. Aguirre and B.C Grau and K. Eckert and J. Euzenat and Ferrara Alfio and {van Hage}, Willem and L. Hollink and E. Jimenez-Ruiz and C. Meilicke and A. Nikolov and D. Ritze and F. Scharffe and P. Shvaiko and O. Sváb-Zamazal and C. Trojahn and B. Zapilko",
  year      = "2013",
  pages     = "73--115",
  booktitle = "7th ISWC workshop on ontology matching (OM)",
}

@inbook{efb46ed16f7047e2b077a25922736d00,
  title     = "Rough Set Semantics for Identity on the Web",
  author    = "{van Beek}, W. and K.S. Schlobach",
  year      = "2013",
  booktitle = "2013 AAAI Fall Symposium Series",
}

@inbook{d619971b9e074c95b786a3eca3b4ff1f,
  title     = "Rough Set Semantics for Identity on the Web",
  abstract  = "Identity relations are at the foundation of the Linked Open Data initiative and on the Semantic Web in general. They allow the interlinking of alternative descriptions of the same thing. However, many practical uses of owl:sameAs are known to violate its formal semantics. We propose a method that assigns meaning to (the subrelations of) an identity relation using the predicates of the dataset schema. Applications of this approach include automated suggestions for asserting/retracting identity pairs and quality assessment. We also describe an experimental design for this approach.",
  author    = "Wouter Beek and Stefan Schlobach and {van Harmelen}, Frank",
  year      = "2013",
  pages     = "10--13",
  editor    = "{van Harmelen}, F. and J. Hendler and P Hitzler and K Janowicz",
  booktitle = "AAAI Fall Symposium Semantics for Big Data",
  publisher = "AAAI",
}

Identity relations are at the foundation of the Linked Open Data initiative and on the Semantic Web in general. They allow the interlinking of alternative descriptions of the same thing. However, many practical uses of owl:sameAs are known to violate its formal semantics. We propose a method that assigns meaning to (the subrelations of) an identity relation using the predicates of the dataset schema. Applications of this approach include automated suggestions for asserting/retracting identity pairs and quality assessment. We also describe an experimental design for this approach.

@article{5c65a8ca57b84d3b8144d1238323ceca,
  title     = "Rule-based Formalization of Eligibility Criteria for Clinical Trials",
  author    = "Z. Huang and {ten Teije}, A.C.M. and {van Harmelen}, F.A.H.",
  note      = "Proceedings title: Artificial Intelligence in Medicine Publisher: Springer Editors: N Peek, R Morales, M Peleg",
  year      = "2013",
  volume    = "7885",
  pages     = "38--47",
  journal   = "Lecture Notes in Computer Science",
  issn      = "0302-9743",
  publisher = "Springer Verlag",
}

@inbook{7185f4a362534934bac69d8b0c40f420,
  title     = "Semantically Modeling Mobile Phone Data for Urban Computing",
  author    = "H. Wang and Z. Huang and N. Zhong",
  year      = "2013",
  booktitle = "Proceedings of the 2013 International Conference on Active Media Technology (AMT2013)",
  publisher = "Springer",
}

@inbook{7a0fd834f646426d8e921691cc2d7cab,
  title     = "Semantic and Qualitative Spatial Reasoning based Road Network Modeling",
  author    = "X. Zhang and Z. Huang",
  year      = "2013",
  booktitle = "Proceedings of 2013 International Workshop on Big Web Data",
  publisher = "Springer",
}

@inbook{71265264f4d14daab72cb8509624aa67,
  title     = "Semantic Approach for Rational Use of Antibiotics: A Perspective from Clinical Research",
  author    = "X. Hua and Z. Huang and R. Yang and Q. Hu",
  year      = "2013",
  booktitle = "Proceedings of 2013 International Workshop on Semantic Technology for eHealth (STeH2013)",
  publisher = "Springer",
}

@article{827ae4d020e343c4863bad421de8613c,
  title     = "SemanticCT: A Semantically-Enabled System for Clinical Trials",
  author    = "Z. Huang and {ten Teije}, A.C.M. and {van Harmelen}, F.A.H.",
  note      = "Proceedings title: Process Support and Knowledge Representation in Health Care Publisher: Springer",
  year      = "2013",
  volume    = "8268",
  pages     = "11--25",
  journal   = "Lecture Notes in Computer Science",
  issn      = "0302-9743",
  publisher = "Springer Verlag",
  number    = "iss",
}

@inbook{47e6380dcef045b0a1e8b95e8729e4c0,
  title     = "Semantic Integration of Patient Data and Quality Indicators based on openEHR Archetypes",
  author    = "K. Dentler and {ten Teije}, A. and R. Cornet and {De Keizer}, N.",
  year      = "2013",
  isbn      = "9783642364389",
  series    = "2013",
  pages     = "85--97",
  booktitle = "Joint Workshop on Process-oriented Information Systems in Healthcare (ProHealth) and Knowledge Representation for Health Care (KRH4C)",
}

@inbook{a27bf65091054e30b0d10bdc36cd7812,
  title     = "Semantic Transformation and Generation for Guidelines of Rational Use of Antibiotics",
  author    = "J. Gu and Q. Hu and Z. Huang",
  year      = "2013",
  pages     = "20--24",
  booktitle = "Journal of China Digital Medicine, Vol.8, No.4",
}

@misc{499f192ce0124f7d8a23b831ffc4eaa4,
  title  = "SeSRUA: a Prototype for Intelligent Monitoring on Rational Use of Antibiotics",
  author = "J. Gu and Z. Huang",
  note   = "Proceedings title: Proceedings of the 3rd Joint International Semantic Technology Conference (JIST2013) Publisher: Springer",
  year   = "2013",
}

@inbook{f72142d4073d4b14be4b6cda23da0219,
  title     = "Stay Point Identification of Mobile Phone Trajectory",
  author    = "R. Du and J. Huang and H. Wang and N. Zhong and Z. Huang",
  year      = "2013",
  pages     = "26--30",
  booktitle = "Proceedings of 2013 China Web Intelligence Conference(CRSSC-CWI-CGrC2013),",
}

@inbook{e6d191f6fd8744bd9b476c79ba20be76,
  title     = "The Design and Implementation of Modern Column-Oriented Database Systems",
  author    = "D. Abadi and P.A. Boncz and S. Harizopoulos and S. Idreos and S. Madden",
  year      = "2013",
  series    = "5",
  publisher = "NOW Publishers",
  number    = "3",
  pages     = "297--280",
  booktitle = "Foundations and Trends in Databases",
}

@inbook{b14764c9d8b84f6693aeb52342126caa,
  title     = "The Linked Data Benchmark Council Project",
  author    = "P.A. Boncz and I. Fundulaki and A. Gubichev and {Larriba Pey}, J. and T. Neumann",
  year      = "2013",
  pages     = "121--129",
  booktitle = "Datenbank-Spektrum July 2013, Volume 13, Issue 2",
  publisher = "Springer Verlag",
}

@inbook{d5c0fdc5d7844aac9a5be1ed8b03bdf6,
  title     = "Towards automatic patient eligibility assessment: from free-text criteria to queries",
  abstract  = "The presented work contributes to bridging the representation of clinical trials and patient data. Our ultimate goal is to support the trial recruitment, by automating the process of formalizing eligibility criteria of clinical trials, starting from free text of criteria and leading to a computable representation. This paper discusses the final step in the pipeline i.e. generating queries from the structured representation consisting of detected patterns and semantic entities. The queries allow to evaluate patient eligibility for a given trial. To enable easy incorporation of semantic reasoning using medical ontologies, we built the queries in SPARQL and use the OWL representation of one the standards for patient data storage - openEHR archetypes and NCI ontology. The available public repository of archetypes and the expressivity of SPARQL allow to create template queries for the majority of patterns. © 2013 Springer-Verlag.",
  author    = "K. Milian and {ten Teije}, A.C.M.",
  year      = "2013",
  doi       = "10.1007/978-3-642-38326-7_12",
  isbn      = "9783642383250",
  pages     = "78--83",
  editor    = "N. Peek and M. Morales and M. Peleg",
  booktitle = "14th Conference on Artificial Intelligence in Medicine (AIME2013)",
  publisher = "Springer",
}

The presented work contributes to bridging the representation of clinical trials and patient data. Our ultimate goal is to support the trial recruitment, by automating the process of formalizing eligibility criteria of clinical trials, starting from free text of criteria and leading to a computable representation. This paper discusses the final step in the pipeline i.e. generating queries from the structured representation consisting of detected patterns and semantic entities. The queries allow to evaluate patient eligibility for a given trial. To enable easy incorporation of semantic reasoning using medical ontologies, we built the queries in SPARQL and use the OWL representation of one the standards for patient data storage - openEHR archetypes and NCI ontology. The available public repository of archetypes and the expressivity of SPARQL allow to create template queries for the majority of patterns. © 2013 Springer-Verlag.

@inbook{567a5dbf15aa43a5bcc58535a8a38ed9,
  title     = "TPC-H Analyzed: Hidden Messages And Lessons Learned From An Influential Benchmark",
  author    = "P.A. Boncz and T. Neumann and O. Erling",
  year      = "2013",
  editor    = "M. Poess and R. Niambar",
  booktitle = "Proceedings of the TPC Technology Conference on Performance Evaluation $1amp; Benchmarking (TPCTC, 2013)",
  publisher = "Springer Verlag",
}

@inbook{4027a59b0e8c47a58608b9e0939ecf3c,
  title     = "Using Semantic Technology for Automatic Verification of Road Signs",
  author    = "D. Xu and Z. Huang and Q. Liu",
  year      = "2013",
  booktitle = "Proceedings of 2013 International Workshop on Semantic Technology for Smart City",
  publisher = "Springer",
}

@inbook{ea538ee5ab084d968ad1e30543d6d88a,
  title     = "Visual Interface Tools for Advanced Patient Data Generator",
  author    = "M. Zhang and Z. Huang and J. Gu",
  year      = "2013",
  pages     = "26--30",
  booktitle = "Journal of China Digital Medicine, Vol.8, No.5",
}

@inbook{67f3c7cdbb1b4f54a1bdd6c895722c11,
  title     = "Who are we talking about?: identifying scientific populations online",
  author    = "J.M. Birkholz and S. Wang and P.T. Groth and S. Magliacane",
  year      = "2013",
  booktitle = "Chinese Semantic Web Symposium & Web Science Conference Proceedings",
  publisher = "Springer Publications",
}

@inbook{27bfea5c82174089bb1ae573e8a27dbc,
  title     = "YASGUI: Not Just Another SPARQL Client",
  author    = "L.J. Rietveld and R. Hoekstra",
  year      = "2013",
  booktitle = "Proceedings of the ESWC2013 workshop on Services and Applications over Linked APIs and Data (SALAD 2013)",
}

@inbook{83f7e8f504a94918ad49af0d3b00ced4,
  title     = "YASGUI: Not Just Another SPARQL Client",
  author    = "L.J. Rietveld and R.J. Hoekstra",
  year      = "2013",
  editor    = "M Maleshkova and R. Verborgh and T. Steiner",
  booktitle = "Proceedings on the ESWC2013 Workshop on Services and Applications over Linked APIs and Data",
}

@article{34a3d04075e549dbac0a02e26910a7c4,
  title     = "Erratum: WebPIE: A Web-scale Parallel Inference Engine using MapReduce (Journal of Web Semantics (2012) 10 (59-75))",
  author    = "Jacopo Urbani and Spyros Kotoulas and Jason Maassen and {Van Harmelen}, Frank and Henri Bal",
  year      = "2012",
  month     = "12",
  doi       = "10.1016/j.websem.2012.09.005",
  volume    = "17",
  pages     = "44",
  journal   = "Journal of Web Semantics",
  issn      = "1570-8268",
  publisher = "Elsevier",
}

@article{6acccbd7ec724673ad91a05f587f628c,
  title     = "A Legal Case OWL Ontology with an Instantiation of Popov v. Hayashi",
  abstract  = "The paper provides an OWL ontology for legal cases with an instantiation of the legal case Popov v. Hayashi. The ontology makes explicit the conceptual knowledge of the legal case domain, supports reasoning about the domain, and can be used to annotate the text of cases, which in turn can be used to populate the ontology. A populated ontology is a case base which can be used for information retrieval, information extraction, and case based reasoning. The ontology contains not only elements for indexing the case (e.g. the parties, jurisdiction, and date), but as well elements used to reason to a decision such as argument schemes and the components input to the schemes. We use the Protégé ontology editor and knowledge acquisition system, current guidelines for ontology development, and tools for visual and linguistic presentation of the ontology. © 2012 Springer Science+Business Media B.V.",
  author    = "A.Z. Wyner and R.J. Hoekstra",
  year      = "2012",
  doi       = "10.1007/s10506-012-9119-6",
  journal   = "Artificial Intelligence and Law",
  issn      = "0924-8463",
  publisher = "Springer Netherlands",
}

The paper provides an OWL ontology for legal cases with an instantiation of the legal case Popov v. Hayashi. The ontology makes explicit the conceptual knowledge of the legal case domain, supports reasoning about the domain, and can be used to annotate the text of cases, which in turn can be used to populate the ontology. A populated ontology is a case base which can be used for information retrieval, information extraction, and case based reasoning. The ontology contains not only elements for indexing the case (e.g. the parties, jurisdiction, and date), but as well elements used to reason to a decision such as argument schemes and the components input to the schemes. We use the Protégé ontology editor and knowledge acquisition system, current guidelines for ontology development, and tools for visual and linguistic presentation of the ontology. © 2012 Springer Science+Business Media B.V.

@inbook{416651e4d3bd4ac28a667b0dcda42a48,
  title     = "A method of contrastive reasoning with inconsistent ontologies,",
  author    = "J. Fang and Z. Huang and {van Harmelen}, F.",
  year      = "2012",
  pages     = "1--16",
  booktitle = "the Semantic Web",
  publisher = "Springer",
}

@inbook{e8dd6e2eebb1487d931f37587c36a5ea,
  title     = "An efficient approach to debugging ontologies based on patterns",
  author    = "Q. Ji and Z. Gao and Z. Huang and Man Zhu",
  year      = "2012",
  pages     = "425--433",
  booktitle = "the Semantic Web",
  publisher = "Springer",
}

@inbook{797686d3a7d84ebb9571f182d11ebfa3,
  title     = "An Introduction to Semantic Technology",
  author    = "Z. Huang and Jinshong Li",
  year      = "2012",
  isbn      = "9787308101295",
  pages     = "1--12",
  editor    = "Jinshong Li and Z Huang",
  booktitle = "Semantic Technology for Biomedical Science",
  publisher = "Zhejiang University Press",
}

@inbook{c248a58a8b534fe7b25aee70a2275a10,
  title     = "Annotating Evidence Based Clinical Guidelines -- A Lightweight Ontology",
  author    = "R.J. Hoekstra and {de Waard}, A. and R. Vdovjak",
  year      = "2012",
  editor    = "A Paschke and A Burger and P Roma",
  booktitle = "Proceedings of SWAT4LS 2012",
}

@book{c6171b88c2834c5988ce6f321165c8d2,
  title     = "A Semantic Web Primer",
  author    = "G. Antoniou and P.T. Groth and {van Harmelen}, F. and R.J. Hoekstra",
  year      = "2012",
  isbn      = "0262018284",
  publisher = "MIT Press",
}

@article{d72c731ae13c40f29035d695cedac026,
  title     = "Automated Evaluation of Annotators for Museum Collections using Subjective Logic",
  author    = "D. Ceolin and A. Nottamkandath and W.J. Fokkink",
  note      = "Proceedings title: Trust Management VI - 6th IFIP WG 11.11 International Conference Publisher: Springer ISBN: 978-3-642-29851-6 Editors: D. Patel, H. McKnight, R. Moona, T. Dimitrakos",
  year      = "2012",
  volume    = "374",
  pages     = "232--239",
  journal   = "IFIP",
  issn      = "1571-5736",
  publisher = "Springer New York",
}

@article{e6442fe4d9fc457fad3ecb8c9594355a,
  title     = "Automatic Extraction of Legal Concepts and Definitions",
  abstract  = "In this paper we present the results of an experiment in automatic concept and definition extraction from written sources of law using relatively simple natural language and standard semantic web technology. The software was tested on six laws from the tax domain. © 2012 The authors and IOS Press. All rights reserved.",
  author    = "R.G.F. Winkels and R.J. Hoekstra",
  note      = "Proceedings title: Legal Knowledge and Information Systems, Jurix 2012: the Twenty-Fifth Annual International Conference Publisher: IOS Press Place of publication: Amsterdam ISBN: 978-1-61499-166-3 Editors: B. Schäfer",
  year      = "2012",
  doi       = "10.3233/978-1-61499-167-0-157",
  volume    = "250",
  pages     = "157--166",
  journal   = "Frontiers in Artificial Intelligence and Applications",
  issn      = "0922-6389",
  publisher = "IOS Press",
}

In this paper we present the results of an experiment in automatic concept and definition extraction from written sources of law using relatively simple natural language and standard semantic web technology. The software was tested on six laws from the tax domain. © 2012 The authors and IOS Press. All rights reserved.

@inbook{b3749adf98ef451195432044f472ea7b,
  title     = "Automatic Metadata Annotation through Reconstructing Provenance",
  author    = "P.T. Groth and S. Magliacane and Y Gil",
  year      = "2012",
  booktitle = "Third International Workshop on the role of Semantic Web in Provenance Management",
  publisher = "CEUR Proceedings",
}

@inbook{6f37074ad9ad438f9b763178fbc3b8a1,
  title     = "Building a Library of Eligibility Criteria to support design of clinical trials",
  author    = "K. Milian and A. Bucur and {van Harmelen}, F.",
  year      = "2012",
  isbn      = "9783642338755",
  pages     = "327--336",
  editor    = "{ten Teije}, A and J Völker and S Handschuh and H Stuckenschmidt and M d’Acquin and A Nikolov and N Aussenac-Gilles and N Hernandez",
  booktitle = "Knowledge Engineering and Knowledge Management",
  publisher = "Springer Link",
}

@article{96e6571946374e9cb3b98d46b8819c80,
  title     = "Capturing Common Knowledge about Tasks",
  abstract  = "Although to-do lists are a ubiquitous form of personal task management, there has been no work on intelligent assistance to automate, elaborate, or coordinate a users to-dos. Our research focuses on three aspects of intelligent assistance for to-dos. We investigated the use of intelligent agents to automate to-dos in an office setting. We collected a large corpus from users and developed a paraphrase-based approach to matching agent capabilities with to-dos. We also investigated to-dos for personal tasks and the kinds of assistance that can be offered to users by elaborating on them on the basis of substep knowledge extracted from the Web. Finally, we explored coordination of user tasks with other users through a to-do management application deployed in a popular social networking site. We discuss the emergence of Social Task Networks, which link users tasks to their social network as well as to relevant resources on the Web. We show the benefits of using common sense knowledge to interpret and elaborate to-dos. Conversely, we also show that to-do lists are a valuable way to create repositories of common sense knowledge about tasks. © 2012 ACM.",
  author    = "Y Gil and V Ratnakar and T Chklovski and P.T. Groth and D Vrandecic",
  year      = "2012",
  doi       = "10.1145/2362394.2362397",
  volume    = "2",
  pages     = "1--35",
  journal   = "ACM Transactions on Interactive Intelligent Systems",
  issn      = "2160-6455",
  publisher = "Association for Computing Machinery (ACM)",
  number    = "3",
}

Although to-do lists are a ubiquitous form of personal task management, there has been no work on intelligent assistance to automate, elaborate, or coordinate a users to-dos. Our research focuses on three aspects of intelligent assistance for to-dos. We investigated the use of intelligent agents to automate to-dos in an office setting. We collected a large corpus from users and developed a paraphrase-based approach to matching agent capabilities with to-dos. We also investigated to-dos for personal tasks and the kinds of assistance that can be offered to users by elaborating on them on the basis of substep knowledge extracted from the Web. Finally, we explored coordination of user tasks with other users through a to-do management application deployed in a popular social networking site. We discuss the emergence of Social Task Networks, which link users tasks to their social network as well as to relevant resources on the Web. We show the benefits of using common sense knowledge to interpret and elaborate to-dos. Conversely, we also show that to-do lists are a valuable way to create repositories of common sense knowledge about tasks. © 2012 ACM.

@inbook{9fde87b3c1074321bc6bb454aacbedc0,
  title     = "Contagion of Habitual Behaviour in Social Networks: an Agent-Based Model",
  author    = "M.C.A. Klein and N.M. Mogles and J. Treur and {van Wissen}, A.",
  year      = "2012",
  booktitle = "Proceedings of the 4th International Conference on Social Computing",
  publisher = "IEEE Computer Society Press",
}

@article{61de931a71f5471f99b91c13c31fbca9,
  title     = "Dealing with the Messiness of the Web of Data",
  author    = "K.S. Schlobach and C.A. Knoblock",
  year      = "2012",
  doi       = "10.1016/j.websem.2012.05.004",
  volume    = "14",
  journal   = "Journal of Web Semantics",
  issn      = "1570-8268",
  publisher = "Elsevier",
  number    = "1",
}

@inbook{5cd7b28ffb8f45dca28c9969aebecff7,
  title     = "Efficient Execution of Top-K SPARQL Queries",
  author    = "S. Magliacane and A. Bozzon and {Della Valle}, E",
  year      = "2012",
  booktitle = "ISWC 2012",
}

@inbook{a4cf3abc5c9f4e67ad01a0e71b8aa1f4,
  title     = "Evolutionary and Swarm Computing for scaling up the Semantic Web",
  author    = "C.D.M. Gueret and K.S. Schlobach and K. Dentler and M.C. Schut",
  year      = "2012",
  booktitle = "Proceedings of the 24th Benelux Conference on Artificial Intelligence (BNAIC 2012)",
}

@inbook{fbacf84931b147dfaa711d0ec75dd8a0,
  title     = "Evolutionary and Swarm computing for the Semantic Web",
  abstract  = "The Semantic Web has become a dynamic and enormous network of typed links between data sets stored on different machines. These data sets are machine readable and unambiguously interpretable, thanks to their underlying standard representation languages. The expressiveness and flexibility of the publication model of Linked Data has led to its widespread adoption and an ever increasing publication of semantically rich data on the Web. This success however has started to create serious problems as the scale and complexity of information outgrows the current methods in use, which are mostly based on database technology, expressive knowledge representation formalism and high-performance computing. We argue that methods from computational intelligence can play an important role in solving these problems. In this paper we introduce and systemically discuss the typical application problems on the Semantic Web and argue that the existing approaches to address their underlying reasoning tasks consistently fail because of the increasing size, dynamicity and complexity of the data. For each of these primitive reasoning tasks we will discuss possible problem solving methods grounded in Evolutionary and Swarm computing, with short descriptions of existing approaches. Finally, we will discuss two case studies in which we successfully applied soft computing methods to two of the main reasoning tasks; an evolutionary approach to querying, and a swarm algorithm for entailment. © 2012 IEEE.",
  author    = "C.D.M. Gueret and K.S. Schlobach and K. Dentler and M.C. Schut and A.E. Eiben",
  year      = "2012",
  doi       = "10.1109/MCI.2012.2188583",
  series    = "2",
  pages     = "16--31",
  booktitle = "IEEE Computational Intelligence Magazine",
}

The Semantic Web has become a dynamic and enormous network of typed links between data sets stored on different machines. These data sets are machine readable and unambiguously interpretable, thanks to their underlying standard representation languages. The expressiveness and flexibility of the publication model of Linked Data has led to its widespread adoption and an ever increasing publication of semantically rich data on the Web. This success however has started to create serious problems as the scale and complexity of information outgrows the current methods in use, which are mostly based on database technology, expressive knowledge representation formalism and high-performance computing. We argue that methods from computational intelligence can play an important role in solving these problems. In this paper we introduce and systemically discuss the typical application problems on the Semantic Web and argue that the existing approaches to address their underlying reasoning tasks consistently fail because of the increasing size, dynamicity and complexity of the data. For each of these primitive reasoning tasks we will discuss possible problem solving methods grounded in Evolutionary and Swarm computing, with short descriptions of existing approaches. Finally, we will discuss two case studies in which we successfully applied soft computing methods to two of the main reasoning tasks; an evolutionary approach to querying, and a swarm algorithm for entailment. © 2012 IEEE.

@inbook{ddcaf8c9de6e4303bd0c571783a015c7,
  title     = "Extending SPARQL algebra to support efficient evaluation of top-k SPARQL queries",
  author    = "A. Bozzon and {Della Valle}, E and S. Magliacane",
  year      = "2012",
  pages     = "143--156",
  booktitle = "Search Computing",
  publisher = "Springer",
}

@inbook{d7eda29c70c0423db2ba74dd4b550f25,
  title     = "Formalizalization of clinical trial eligibility criteria: Evaluation of a pattern-based approach",
  abstract  = "The semi-automatic evaluation of eligibility criteria can facilitate the recruitment for clinical trials, timely completion of studies and generation of clinical evidence about new approaches to treatment, prevention and diagnosis. Because eligibility criteria are represented as free text, automatically extracting their meaning and evaluating them for a particular patient is challenging. This paper presents our approach to the problem of automatic interpretation of criteria meaning. It is based on detecting in text semantic entities (diseases, treatment, measurements etc.) using ontology annotators and semantic taggers, and detecting predefined patterns providing the contextual information in which these entities occur. Evaluation of the approach is the main subject of the paper. It covers several aspects: precision and recall of the pattern detection algorithm and the assessment of the implications of using the identified patterns to find potential candidates. It was performed manually using a subset of patterns and randomly selected 33 trials from ClinicalTrials.gov. The average precision and recall of pattern detection algorithm calculated for selected patterns is 0.9 and 0.91, meaning that in most cases using the patterns can lead to correct interpretation of criteria and can support patient recruitment. © 2012 IEEE.",
  author    = "K. Milian and A. Bucur and {ten Teije}, A.C.M.",
  year      = "2012",
  doi       = "10.1109/BIBM.2012.6392733",
  pages     = "1--4",
  editor    = "J. Gao and R. Alhaij and W. Dubitzky and L. Ungar and C. Wu and A, Christianson and M. Liebman and X. Hu",
  booktitle = "IEEE International Conference of Bioinformatics and Biomedicine 2012",
}

The semi-automatic evaluation of eligibility criteria can facilitate the recruitment for clinical trials, timely completion of studies and generation of clinical evidence about new approaches to treatment, prevention and diagnosis. Because eligibility criteria are represented as free text, automatically extracting their meaning and evaluating them for a particular patient is challenging. This paper presents our approach to the problem of automatic interpretation of criteria meaning. It is based on detecting in text semantic entities (diseases, treatment, measurements etc.) using ontology annotators and semantic taggers, and detecting predefined patterns providing the contextual information in which these entities occur. Evaluation of the approach is the main subject of the paper. It covers several aspects: precision and recall of the pattern detection algorithm and the assessment of the implications of using the identified patterns to find potential candidates. It was performed manually using a subset of patterns and randomly selected 33 trials from ClinicalTrials.gov. The average precision and recall of pattern detection algorithm calculated for selected patterns is 0.9 and 0.91, meaning that in most cases using the patterns can lead to correct interpretation of criteria and can support patient recruitment. © 2012 IEEE.

@inbook{a63e490a3998468b91822633ee456c4a,
  title     = "Formalization of clinical trial eligibility criteria: Evaluation of a pattern-based approach",
  author    = "K. Milian and A. Bucur and {ten Teije}, A. and {van Harmelen}, F.",
  year      = "2012",
  pages     = "495--498",
  editor    = "J Gao and R Alhaij and W Dubitzky and L Ungar and C Wu and A Christianson and M Liebman and X Hu",
  booktitle = "2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
  publisher = "IEEE Computer Society Technical Committee on Bioinformatics",
}

@inbook{0cce0204a6f44e39a461a845757f99c3,
  title     = "Formal Verification of Data Provenance Records",
  author    = "S. Klarman and S. Schlobach and L. Serafini",
  year      = "2012",
  booktitle = "Proc. of the 11th International Semantic Web Conference (ISWC-12)",
}

@inbook{da8e355aef0f4fbfa360cea00b97cbb1,
  title     = "Formal Verification of Data Provenance Records",
  author    = "S. Klarman and K.S. Schlobach and L. Serafini",
  year      = "2012",
  booktitle = "Proceedings of the 11th International Semantic Web Conference (ISWC-12)",
  publisher = "Springer",
}

@article{bbff608b8aae4bfe9ef062f842c3d8de,
  title     = "From Cooperative Scans To Predictive Buffer Management",
  author    = "M. Switakowski and P.A. Boncz and M. Zukowski",
  year      = "2012",
  doi       = "10.14778/2367502.2367515",
  volume    = "5",
  pages     = "1748--1758",
  journal   = "PVLDB",
  issn      = "2150-8097",
  publisher = "Very Large Data Base Endowment Inc.",
  number    = "12",
}

@inbook{1b1dceaa33a744c2aba52356ecf4de1c,
  title     = "From X100 to Vectorwise: opportunities, challenges and things most researchers do not think about",
  author    = "M. Zukowski and P.A. Boncz",
  year      = "2012",
  booktitle = "Proc. ACM SIGMOD",
}

@inbook{56ccdaac43e642d8b3775ea52b8a0aee,
  title     = "Heuristics-based query optimisation for SPARQL",
  author    = "P. Tsialiamanis and L. Sidirourgos and V. Christophides and I. Fundulaki and P.A. Boncz",
  year      = "2012",
  booktitle = "Proc. International Conference on Extending Database Technology (EDBT)",
}

@article{ecad8c82529d43e5b56b5f2845e7fc7c,
  title     = "Human-Agent Team Formation in Dynamic Environments",
  abstract  = "Teamwork between humans and computer agents has become increasingly prevalent. This paper presents a behavioral study of fairness and trust in a heterogeneous setting comprising both computer agents and human participants. It investigates people's choice of teammates and their commitment to their teams in a dynamic environment in which actions occur at a fast pace and decisions are made within tightly constrained time frames, under conditions of uncertainty and partial information. In this setting, participants could form teams by negotiating over the division of a reward for the successful completion of a group task. Participants could also choose to defect from their existing teams in order to join or create other teams. Results show that when people form teams, they offer significantly less reward to agents than they offer to people. The most significant factor affecting people's decisions whether to defect from their existing teams is the extent to which they had successful previous interactions with other team members. Also, there is no significant difference in people's rate of defection from agent-led teams as compared to their defection from human-led teams. These results are significant for agent designers and behavioral researchers who study human-agent interactions. © 2011 Elsevier Ltd. All rights reserved.",
  author    = "{van Wissen}, A. and Y. Gal and B. Kamphorst and V. Dignum",
  year      = "2012",
  doi       = "10.1016/j.chb.2011.08.006",
  volume    = "28",
  pages     = "23--33",
  journal   = "Computers in Human Behavior",
  issn      = "0747-5632",
  publisher = "Pergamon",
  number    = "1",
}

Teamwork between humans and computer agents has become increasingly prevalent. This paper presents a behavioral study of fairness and trust in a heterogeneous setting comprising both computer agents and human participants. It investigates people's choice of teammates and their commitment to their teams in a dynamic environment in which actions occur at a fast pace and decisions are made within tightly constrained time frames, under conditions of uncertainty and partial information. In this setting, participants could form teams by negotiating over the division of a reward for the successful completion of a group task. Participants could also choose to defect from their existing teams in order to join or create other teams. Results show that when people form teams, they offer significantly less reward to agents than they offer to people. The most significant factor affecting people's decisions whether to defect from their existing teams is the extent to which they had successful previous interactions with other team members. Also, there is no significant difference in people's rate of defection from agent-led teams as compared to their defection from human-led teams. These results are significant for agent designers and behavioral researchers who study human-agent interactions. © 2011 Elsevier Ltd. All rights reserved.

@article{1f65eccd5a914b5eb02bb1b34493cc43,
  title     = "Instance-Based Ontology Matching by Instance Enrichment",
  abstract  = "The ontology matching (OM) problem is an important barrier to achieve true Semantic Interoperability. Instance-based ontology matching (IBOM) uses the extension of concepts, the instances directly associated with a concept, to determine whether a pair of concepts is related or not. While IBOM has many strengths it requires instances that are associated with concepts of both ontologies, (i.e) dually annotated instances. In practice, however, instances are often associated with concepts of a single ontology only, rendering IBOM rarely applicable. In this paper we discuss a method that enables IBOM to be used on two disjoint datasets, thus making it far more generically applicable. This is achieved by enriching instances of each dataset with the conceptual annotations of the most similar instances from the other dataset, creating artificially dually annotated instances. We call this technique instance-based ontology matching by instance enrichment (IBOMbIE). We have applied the IBOMbIE algorithm in a real-life use-case where large datasets are used to match the ontologies of European libraries. Existing gold standards and dually annotated instances are used to test the impact and significance of several design choices of the IBOMbIE algorithm. Finally, we compare the IBOMbIE algorithm to other ontology matching algorithms.",
  author    = "B. Schopman and S. Wang and A.H.J.C.A. Isaac and K.S. Schlobach",
  year      = "2012",
  doi       = "10.1007/s13740-012-0011-z",
  volume    = "1",
  pages     = "219--236",
  journal   = "Journal on Data Semantics",
  issn      = "1861-2032",
  publisher = "Springer International Publishing AG",
  number    = "4",
}

The ontology matching (OM) problem is an important barrier to achieve true Semantic Interoperability. Instance-based ontology matching (IBOM) uses the extension of concepts, the instances directly associated with a concept, to determine whether a pair of concepts is related or not. While IBOM has many strengths it requires instances that are associated with concepts of both ontologies, (i.e) dually annotated instances. In practice, however, instances are often associated with concepts of a single ontology only, rendering IBOM rarely applicable. In this paper we discuss a method that enables IBOM to be used on two disjoint datasets, thus making it far more generically applicable. This is achieved by enriching instances of each dataset with the conceptual annotations of the most similar instances from the other dataset, creating artificially dually annotated instances. We call this technique instance-based ontology matching by instance enrichment (IBOMbIE). We have applied the IBOMbIE algorithm in a real-life use-case where large datasets are used to match the ontologies of European libraries. Existing gold standards and dually annotated instances are used to test the impact and significance of several design choices of the IBOMbIE algorithm. Finally, we compare the IBOMbIE algorithm to other ontology matching algorithms.

@inbook{5771d761d4394550bb4767e72775ee43,
  title     = "Introduction to Scalable Semantic Processing",
  author    = "Z. Huang and N. Zhong",
  year      = "2012",
  isbn      = "9787040362466",
  pages     = "1--13",
  booktitle = "Scalable Semantic Data Processing",
  publisher = "Higher Education Press",
}

@book{3af6b37551a14a2f93bac257cd0f7c54,
  title     = "Knowledge Representation for Health-Care",
  author    = "R. David and {ten Teije}, A.C.M. and S Miksch",
  year      = "2012",
  isbn      = "9783642276972",
  series    = "LNAI",
  publisher = "Springer",
  number    = "6924",
}