Towards Easy Vocabulary Drafts with Neologism 2.0.
Lipp, J.; Gleim, L.; Cochez, M.; Dimitriadis, I.; Ali, H.; Alvarez, D. H.; Lange, C.; and Decker, S.
In Verborgh, R.; Dimou, A.; Hogan, A.; d'Amato , C.; Tiddi, I.; Bröring, A.; Mayer, S.; Ongenae, F.; Tommasini, R.; and Alam, M., editor(s),
The Semantic Web: ESWC 2021 Satellite Events, pages 21–26, Cham, 2021. Springer International Publishing
![Towards Easy Vocabulary Drafts with Neologism 2.0 [link]](//bibbase.org/img/filetypes/link.svg "Paper")
Paper
link
bibtex
abstract
@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.
Knowledge Graphs.
Hogan, A.; Blomqvist, E.; Cochez, M.; D’amato, C.; Melo, G. D.; Gutierrez, C.; Kirrane, S.; Gayo, J. E. L.; Navigli, R.; Neumaier, S.; Ngomo, A. N.; Polleres, A.; Rashid, S. M.; Rula, A.; Schmelzeisen, L.; Sequeda, J.; Staab, S.; and Zimmermann, A.
ACM Comput. Surv., 54(4). July 2021.
Paper
doi
link
bibtex
abstract
@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.
Unsupervised Feature Selection for Efficient Exploration of High Dimensional Data.
Chakrabarti, A.; Das, A.; Cochez, M.; and Quix, C.
In Bellatreche, L.; Dumas, M.; Karras, P.; and Matulevičius, R., editor(s),
Advances in Databases and Information Systems, pages 183–197, Cham, 2021. Springer International Publishing
![Unsupervised Feature Selection for Efficient Exploration of High Dimensional Data [pdf]](//bibbase.org/img/filetypes/pdf.svg "Paper")
Paper
link
bibtex
abstract
@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.
DeepKneeExplainer: Explainable Knee Osteoarthritis Diagnosis From Radiographs and Magnetic Resonance Imaging.
Karim, M. R.; Jiao, J.; Döhmen, T.; Cochez, M.; Beyan, O.; Rebholz-Schuhmann, D.; and Decker, S.
IEEE Access, 9: 39757-39780. 2021.
doi
link
bibtex
@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}}
Query Embedding on Hyper-relational Knowledge Graphs.
Alivanistos, D.; Berrendorf, M.; Cochez, M.; and Galkin, M.
CoRR, abs/2106.08166. 2021.
Paper
link
bibtex
@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}
}
Modular design patterns for hybrid learning and reasoning systems.
van Bekkum, M.; de Boer, M.; van Harmelen, F.; Meyer-Vitali, A.; and ten Teije, A.
Appl. Intell., 51(9): 6528–6546. 2021.
Paper
doi
link
bibtex
@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}
}
Network metrics for assessing the quality of entity resolution between multiple datasets.
Idrissou, A. K.; van Harmelen, F.; and van den Besselaar, P.
Semantic Web, 12(1): 21–40. 2021.
Paper
doi
link
bibtex
@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}
}
Biomedical Dataset Recommendation.
Wang, X.; van Harmelen, F.; and Huang, Z.
In Quix, C.; Hammoudi, S.; and van der Aalst, W. M. P., editor(s),
Proceedings of the 10th International Conference on Data Science, Technology and Applications, DATA 2021, Online Streaming, July 6-8, 2021, pages 192–199, 2021. SCITEPRESS
Paper
doi
link
bibtex
@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}
}
Refining Transitive and Pseudo-Transitive Relations at Web Scale.
Wang, S.; Raad, J.; Bloem, P.; and van Harmelen, F.
In Verborgh, R.; Hose, K.; Paulheim, H.; Champin, P.; Maleshkova, M.; Corcho, Ó.; Ristoski, P.; and Alam, M., editor(s),
The Semantic Web - 18th International Conference, ESWC 2021, Virtual Event, June 6-10, 2021, Proceedings, volume 12731, of
Lecture Notes in Computer Science, pages 249–264, 2021. Springer
Paper
doi
link
bibtex
@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 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.
Martin, A.; Hinkelmann, K.; Fill, H.; Gerber, A.; Lenat, D.; Stolle, R.; and van Harmelen, F.,
editors.
Volume 2846, of CEUR Workshop Proceedings.CEUR-WS.org. 2021.
Paper
link
bibtex
@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}
}
Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases.
van Bekkum, M.; de Boer, M.; van Harmelen, F.; Meyer-Vitali, A.; and ten Teije, A.
CoRR, abs/2102.11965. 2021.
Paper
link
bibtex
@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}
}
Modular design patterns for hybrid learning and reasoning systems.
van Bekkum, M.; de Boer, M.; van Harmelen, F.; Meyer-Vitali, A.; and ten Teije, A.
Appl. Intell., 51(9): 6528–6546. 2021.
Paper
doi
link
bibtex
@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}
}
Preface: AIME 2019.
Riaño, D.; Wilk, S.; and ten Teije, A.
Artif. Intell. Medicine, 115: 102058. 2021.
Paper
doi
link
bibtex
@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}
}
Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases.
van Bekkum, M.; de Boer, M.; van Harmelen, F.; Meyer-Vitali, A.; and ten Teije, A.
CoRR, abs/2102.11965. 2021.
Paper
link
bibtex
@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}
}
Storchastic: A Framework for General Stochastic Automatic Differentiation.
van Krieken, E.; Tomczak, J. M.; and ten Teije, A.
CoRR, abs/2104.00428. 2021.
Paper
link
bibtex
@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}
}
Biomedical dataset recommendation.
Wang, X.; van Harmelen , F.; and Huang, Z.
In Quix, C.; Hammoudi, S.; and van der Aalst , W., editor(s),
Proceedings of the 10th International Conference on Data Science, Technology and Applications, DATA 2021, pages 192–199, 2021. SciTePress
10th International Conference on Data Science, Technology and Applications, DATA 2021 ; Conference date: 06-07-2021 Through 08-07-2021
doi
link
bibtex
abstract
@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.
Predicting the relationships between gut microbiota and mental disorders with knowledge graphs.
Liu, T.; Pan, X.; Wang, X.; Feenstra, K. A.; Heringa, J.; and Huang, Z.
Health Inf. Sci. Syst., 9(1): 3. 2021.
Paper
doi
link
bibtex
@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}
}
Knowledge Graphs of Kawasaki Disease.
Huang, Z.; Hu, Q.; Liao, M.; Miao, C.; Wang, C.; and Liu, G.
Health Inf. Sci. Syst., 9(1): 11. 2021.
Paper
doi
link
bibtex
@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}
}
Biomedical Dataset Recommendation.
Wang, X.; van Harmelen, F.; and Huang, Z.
In Quix, C.; Hammoudi, S.; and van der Aalst, W. M. P., editor(s),
Proceedings of the 10th International Conference on Data Science, Technology and Applications, DATA 2021, Online Streaming, July 6-8, 2021, pages 192–199, 2021. SCITEPRESS
Paper
doi
link
bibtex
@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}
}
Discovering Research Hypotheses in Social Science Using Knowledge Graph Embeddings.
de Haan, R.; Tiddi, I.; and Beek, W.
In Verborgh, R.; Hose, K.; Paulheim, H.; Champin, P.; Maleshkova, M.; Corcho, Ó.; Ristoski, P.; and Alam, M., editor(s),
The Semantic Web - 18th International Conference, ESWC 2021, Virtual Event, June 6-10, 2021, Proceedings, volume 12731, of
Lecture Notes in Computer Science, pages 477–494, 2021. Springer
Paper
doi
link
bibtex
@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}
}
DeepKneeExplainer: Explainable Knee Osteoarthritis Diagnosis From Radiographs and Magnetic Resonance Imaging.
Karim, M. R.; Jiao, J.; Döhmen, T.; Cochez, M.; Beyan, O.; Rebholz-Schuhmann, D.; and Decker, S.
IEEE Access, 9: 39757–39780. 2021.
Paper
doi
link
bibtex
@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}
}
Graph Structures for Knowledge Representation and Reasoning - 6th International Workshop, GKR 2020, Virtual Event, September 5, 2020, Revised Selected Papers.
Cochez, M.; Croitoru, M.; Marquis, P.; and Rudolph, S.,
editors.
Volume 12640, of Lecture Notes in Computer Science.Springer. 2021.
Paper
doi
link
bibtex
@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}
}
Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification.
van Bakel, R.; Aleksiev, T.; Daza, D.; Alivanistos, D.; and Cochez, M.
CoRR, abs/2102.11389. 2021.
Paper
link
bibtex
@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}
}
Secure Evaluation of Knowledge Graph Merging Gain.
Eichenberger, L.; Cochez, M.; Heitmann, B.; and Decker, S.
CoRR, abs/2103.00082. 2021.
Paper
link
bibtex
@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}
}
Query Embedding on Hyper-relational Knowledge Graphs.
Alivanistos, D.; Berrendorf, M.; Cochez, M.; and Galkin, M.
CoRR, abs/2106.08166. 2021.
Paper
link
bibtex
@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}
}
Inductive Entity Representations from Text via Link Prediction.
Daza, D.; Cochez, M.; and Groth, P.
In
Proceedings of The Web Conference 2021, of
WWW '21, New York, NY, USA, 2021. Association for Computing Machinery
Paper
doi
link
bibtex
@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}
}
Complex Query Answering with Neural Link Predictors.
Arakelyan, E.; Daza, D.; Minervini, P.; and Cochez, M.
In
9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021, 2021. OpenReview.net
Paper
link
bibtex
@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}
}
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