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  2021 (33)
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]Paper   link   bibtex   abstract  
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.
Knowledge Graphs [link]Paper   doi   link   bibtex   abstract  
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]Paper   link   bibtex   abstract  
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  
Query Embedding on Hyper-relational Knowledge Graphs. Alivanistos, D.; Berrendorf, M.; Cochez, M.; and Galkin, M. CoRR, abs/2106.08166. 2021.
Query Embedding on Hyper-relational Knowledge Graphs [link]Paper   link   bibtex  
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.
Modular design patterns for hybrid learning and reasoning systems [link]Paper   doi   link   bibtex  
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.
Network metrics for assessing the quality of entity resolution between multiple datasets [link]Paper   doi   link   bibtex  
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
Biomedical Dataset Recommendation [link]Paper   doi   link   bibtex  
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
Refining Transitive and Pseudo-Transitive Relations at Web Scale [link]Paper   doi   link   bibtex  
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.
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 [link]Paper   link   bibtex  
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.
Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases [link]Paper   link   bibtex  
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.
Modular design patterns for hybrid learning and reasoning systems [link]Paper   doi   link   bibtex  
Preface: AIME 2019. Riaño, D.; Wilk, S.; and ten Teije, A. Artif. Intell. Medicine, 115: 102058. 2021.
Preface: AIME 2019 [link]Paper   doi   link   bibtex  
Team Design Patterns for Moral Decisions in Hybrid Intelligent Systems: A Case Study of Bias Mitigation. van Stijn, J. J.; Neerincx, M. A.; ten Teije, A.; and Vethman, S. In Martin, A.; Hinkelmann, K.; Fill, H.; Gerber, A.; Lenat, D.; Stolle, R.; and van Harmelen, F., editor(s), 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, volume 2846, of CEUR Workshop Proceedings, 2021. CEUR-WS.org
Team Design Patterns for Moral Decisions in Hybrid Intelligent Systems: A Case Study of Bias Mitigation [pdf]Paper   link   bibtex  
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.
Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases [link]Paper   link   bibtex  
Storchastic: A Framework for General Stochastic Automatic Differentiation. van Krieken, E.; Tomczak, J. M.; and ten Teije, A. CoRR, abs/2104.00428. 2021.
Storchastic: A Framework for General Stochastic Automatic Differentiation [link]Paper   link   bibtex  
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  
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.
Predicting the relationships between gut microbiota and mental disorders with knowledge graphs [link]Paper   doi   link   bibtex  
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.
Knowledge Graphs of Kawasaki Disease [link]Paper   doi   link   bibtex  
Guest Editorial: Special issue on "Artificial Intelligence in Health Informatics". Siuly, S.; Aickelin, U.; Kabir, M. E.; Huang, Z.; and Zhang, Y. Health Inf. Sci. Syst., 9(1): 23. 2021.
Guest Editorial: Special issue on "Artificial Intelligence in Health Informatics" [link]Paper   doi   link   bibtex  
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
Biomedical Dataset Recommendation [link]Paper   doi   link   bibtex  
Extracting knowledge from Deep Neural Networks through graph analysis. Horta, V. A. C.; Tiddi, I.; Little, S.; and Mileo, A. Future Gener. Comput. Syst., 120: 109–118. 2021.
Extracting knowledge from Deep Neural Networks through graph analysis [link]Paper   doi   link   bibtex  
Learning Profile-Based Recommendations for Medical Search Auto-Complete. Boomgaard, G.; Santamaría, S. B.; Tiddi, I.; Sips, R.; and Szlávik, Z. In Martin, A.; Hinkelmann, K.; Fill, H.; Gerber, A.; Lenat, D.; Stolle, R.; and van Harmelen, F., editor(s), 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, volume 2846, of CEUR Workshop Proceedings, 2021. CEUR-WS.org
Learning Profile-Based Recommendations for Medical Search Auto-Complete [pdf]Paper   link   bibtex  
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
Discovering Research Hypotheses in Social Science Using Knowledge Graph Embeddings [link]Paper   doi   link   bibtex  
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.
DeepKneeExplainer: Explainable Knee Osteoarthritis Diagnosis From Radiographs and Magnetic Resonance Imaging [link]Paper   doi   link   bibtex  
Deep learning-based clustering approaches for bioinformatics. Karim, M. R.; Beyan, O.; Zappa, A.; Costa, I. G.; Rebholz-Schuhmann, D.; Cochez, M.; and Decker, S. Briefings Bioinform., 22(1): 393–415. 2021.
Deep learning-based clustering approaches for bioinformatics [link]Paper   doi   link   bibtex  
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.
Graph Structures for Knowledge Representation and Reasoning - 6th International Workshop, GKR 2020, Virtual Event, September 5, 2020, Revised Selected Papers [link]Paper   doi   link   bibtex  
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.
Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification [link]Paper   link   bibtex  
Secure Evaluation of Knowledge Graph Merging Gain. Eichenberger, L.; Cochez, M.; Heitmann, B.; and Decker, S. CoRR, abs/2103.00082. 2021.
Secure Evaluation of Knowledge Graph Merging Gain [link]Paper   link   bibtex  
Query Embedding on Hyper-relational Knowledge Graphs. Alivanistos, D.; Berrendorf, M.; Cochez, M.; and Galkin, M. CoRR, abs/2106.08166. 2021.
Query Embedding on Hyper-relational Knowledge Graphs [link]Paper   link   bibtex  
kgbench: A Collection of Datasets for Multimodal and Relational Learning on Heterogeneous Knowledge. Wilcke, W.; Bloem, P; van Berkel, L; and de Boer, V In Proceedings of the European Semantic Web Conference 2021, pages 614–630, 2021. Springer
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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
Inductive Entity Representations from Text via Link Prediction [link]Paper   doi   link   bibtex  
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
Complex Query Answering with Neural Link Predictors [link]Paper   link   bibtex  
  2020 (45)
DeepCOVIDExplainer: Explainable COVID-19 Diagnosis from Chest X-ray Images. Karim, M. R.; Döhmen, T.; Cochez, M.; Beyan, O.; Rebholz-Schuhmann, D.; and Decker, S. In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 1034-1037, 2020.
DeepCOVIDExplainer: Explainable COVID-19 Diagnosis from Chest X-ray Images [link]Paper   doi   link   bibtex  
SchemaTree: Maximum-Likelihood Property Recommendation for Wikidata. Gleim, L. C.; Schimassek, R.; Hüser, D.; Peters, M.; Krämer, C.; Cochez, M.; and Decker, S. In Harth, A.; Kirrane, S.; Ngonga Ngomo, A.; Paulheim, H.; Rula, A.; Gentile, A. L.; Haase, P.; and Cochez, M., editor(s), The Semantic Web, pages 179–195, Cham, 2020. Springer International Publishing
link   bibtex   abstract  
Structured query construction via knowledge graph embedding. Wang, R.; Wang, M.; Liu, J.; Cochez, M.; and Decker, S. Knowledge and Information Systems, 62(5): 1819-1846. May 2020.
Structured query construction via knowledge graph embedding [link]Paper   doi   link   bibtex   abstract  
Morphological evolution for pipe inspection using Robot Operating System (ROS). Hallawa, A.; Iacca, G.; Sariman, C.; Rahman, T.; Cochez, M.; and Ascheid, G. Materials and Manufacturing Processes, 35(6): 714-724. 2020.
Morphological evolution for pipe inspection using Robot Operating System (ROS) [link]Paper   doi   link   bibtex  
Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network. Karim, M. R.; Raja Chakravarthi, B.; McCrae, J. P.; and Cochez, M. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pages 390-399, 2020.
Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network [link]Paper   doi   link   bibtex  
A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence. Akata, Z.; Balliet, D.; de Rijke, M.; Dignum, F.; Dignum, V.; Eiben, G.; Fokkens, A.; Grossi, D.; Hindriks, K. V.; Hoos, H. H.; Hung, H.; Jonker, C. M.; Monz, C.; Neerincx, M. A.; Oliehoek, F. A.; Prakken, H.; Schlobach, S.; van der Gaag, L. C.; van Harmelen, F.; van Hoof, H.; van Riemsdijk, B.; van Wynsberghe, A.; Verbrugge, R.; Verheij, B.; Vossen, P.; and Welling, M. Computer, 53(8): 18–28. 2020.
A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence [link]Paper   doi   link   bibtex  
Constructing and Cleaning Identity Graphs in the LOD Cloud. Raad, J.; Beek, W.; van Harmelen, F.; Wielemaker, J.; Pernelle, N.; and Saïs, F. Data Intell., 2(3): 323–352. 2020.