Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges

Authors Claudia d'Amato , Louis Mahon , Pierre Monnin , Giorgos Stamou



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Author Details

Claudia d'Amato
  • University of Bari, Italy
Louis Mahon
  • Oxford University, United Kingdom
Pierre Monnin
  • Université Côte d'Azur, Inria, CNRS, I3S, France
Giorgos Stamou
  • National Technical University of Athens, Greece

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Claudia d'Amato, Louis Mahon, Pierre Monnin, and Giorgos Stamou. Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 8:1-8:35, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/TGDK.1.1.8

Abstract

The graph model is nowadays largely adopted to model a wide range of knowledge and data, spanning from social networks to knowledge graphs (KGs), representing a successful paradigm of how symbolic and transparent AI can scale on the World Wide Web. However, due to their unprecedented volume, they are generally tackled by Machine Learning (ML) and mostly numeric based methods such as graph embedding models (KGE) and deep neural networks (DNNs). The latter methods have been proved lately very efficient, leading the current AI spring. In this vision paper, we introduce some of the main existing methods for combining KGs and ML, divided into two categories: those using ML to improve KGs, and those using KGs to improve results on ML tasks. From this introduction, we highlight research gaps and perspectives that we deem promising and currently under-explored for the involved research communities, spanning from KG support for LLM prompting, integration of KG semantics in ML models to symbol-based methods, interpretability of ML models, and the need for improved benchmark datasets. In our opinion, such perspectives are stepping stones in an ultimate view of KGs as central assets for neuro-symbolic and explainable AI.

Subject Classification

ACM Subject Classification
  • Information systems → World Wide Web
  • Computing methodologies → Artificial intelligence
Keywords
  • Graph-based Learning
  • Knowledge Graph Embeddings
  • Large Language Models
  • Explainable AI
  • Knowledge Graph Completion & Curation

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References

  1. Ralph Abboud, İsmail İlkan Ceylan, Thomas Lukasiewicz, and Tommaso Salvatori. BoxE: A Box Embedding Model for Knowledge Base Completion. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020., 2020. URL: https://proceedings.neurips.cc/paper/2020/hash/6dbbe6abe5f14af882ff977fc3f35501-Abstract.html.
  2. Leonard Adolphs, Shehzaad Dhuliawala, and Thomas Hofmann. How to Query Language Models?, 2021. URL: https://doi.org/10.48550/arXiv.2108.01928.
  3. Aishwarya Agrawal, Jiasen Lu, Stanislaw Antol, Margaret Mitchell, C. Lawrence Zitnick, Devi Parikh, and Dhruv Batra. VQA: Visual Question Answering - www.visualqa.org. International Journal of Computer Vision, 123(1):4-31, 2017. URL: https://doi.org/10.1007/S11263-016-0966-6.
  4. Rakesh Agrawal, Tomasz Imielinski, and Arun N. Swami. Mining Association Rules between Sets of Items in Large Databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, USA, May 26-28, 1993, pages 207-216. ACM Press, 1993. URL: https://doi.org/10.1145/170035.170072.
  5. Muhammad Aurangzeb Ahmad, Carly Eckert, and Ankur Teredesai. Interpretable Machine Learning in Healthcare. In Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2018, Washington, DC, USA, August 29 - September 01, 2018, pages 559-560. ACM, 2018. URL: https://doi.org/10.1145/3233547.3233667.
  6. Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Sahand Sharifzadeh, Volker Tresp, and Jens Lehmann. PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings. Journal of Machine Learning Research, 22:82:1-82:6, 2021. URL: http://jmlr.org/papers/v22/20-825.html.
  7. Safa Alsaidi, Amandine Decker, Puthineath Lay, Esteban Marquer, Pierre-Alexandre Murena, and Miguel Couceiro. A Neural Approach for Detecting Morphological Analogies. In 8th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2021, Porto, Portugal, October 6-9, 2021, pages 1-10. IEEE, 2021. URL: https://doi.org/10.1109/DSAA53316.2021.9564186.
  8. Hiba Arnaout, Simon Razniewski, and Gerhard Weikum. Enriching Knowledge Bases with Interesting Negative Statements. In Conference on Automated Knowledge Base Construction, AKBC 2020, Virtual, June 22-24, 2020, 2020. URL: https://doi.org/10.24432/C5101K.
  9. Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary G. Ives. DBpedia: A Nucleus for a Web of Open Data. In The Semantic Web, 6th International Semantic Web Conference, 2nd Asian Semantic Web Conference, ISWC 2007 + ASWC 2007, Busan, Korea, November 11-15, 2007, volume 4825 of Lecture Notes in Computer Science, pages 722-735. Springer, 2007. URL: https://doi.org/10.1007/978-3-540-76298-0_52.
  10. Daniel Ayala, Agustín Borrego, Inma Hernández, Carlos R. Rivero, and David Ruiz. AYNEC: All You Need for Evaluating Completion Techniques in Knowledge Graphs. In The Semantic Web - 16th International Conference, ESWC 2019, Portorož, Slovenia, June 2-6, 2019, Proceedings, volume 11503 of Lecture Notes in Computer Science, pages 397-411. Springer, 2019. URL: https://doi.org/10.1007/978-3-030-21348-0_26.
  11. Franz Baader, Diego Calvanese, Deborah L. McGuinness, Daniele Nardi, and Peter F. Patel-Schneider, editors. Description Logic Handbook, 2nd edition. Cambridge University Press, 2010. URL: https://doi.org/10.1017/CBO9780511711787.
  12. Samy Badreddine, Artur S. d'Avila Garcez, Luciano Serafini, and Michael Spranger. Logic Tensor Networks. Artificial Intelligence, 303:103649, 2022. URL: https://doi.org/10.1016/J.ARTINT.2021.103649.
  13. Ivana Balazevic, Carl Allen, and Timothy M. Hospedales. Multi-relational Poincaré Graph Embeddings. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019., pages 4465-4475, 2019. URL: https://proceedings.neurips.cc/paper/2019/hash/f8b932c70d0b2e6bf071729a4fa68dfc-Abstract.html.
  14. Tadas Baltrusaitis, Chaitanya Ahuja, and Louis-Philippe Morency. Multimodal Machine Learning: A Survey and Taxonomy. IEEE Transactions on Patterns Analysis and Machine Intelligence, 41(2):423-443, 2019. URL: https://doi.org/10.1109/TPAMI.2018.2798607.
  15. Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, Quyet V. Do, Yan Xu, and Pascale Fung. A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity, 2023. URL: https://doi.org/10.48550/ARXIV.2302.04023.
  16. Shreyansh P. Bhatt, Amit P. Sheth, Valerie L. Shalin, and Jinjin Zhao. Knowledge Graph Semantic Enhancement of Input Data for Improving AI. IEEE Internet Computing, 24(2):66-72, 2020. URL: https://doi.org/10.1109/MIC.2020.2979620.
  17. Peter Bloem, Xander Wilcke, Lucas van Berkel, and Victor de Boer. kgbench: A Collection of Knowledge Graph Datasets for Evaluating Relational and Multimodal Machine Learning. In The Semantic Web - 18th International Conference, ESWC 2021, Virtual Event, June 6-10, 2021, Proceedings, volume 12731 of Lecture Notes in Computer Science, pages 614-630. Springer, 2021. URL: https://doi.org/10.1007/978-3-030-77385-4_37.
  18. Kurt D. Bollacker, Robert P. Cook, and Patrick Tufts. Freebase: A Shared Database of Structured General Human Knowledge. In Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, July 22-26, 2007, Vancouver, British Columbia, Canada, pages 1962-1963. AAAI Press, 2007. URL: http://www.aaai.org/Library/AAAI/2007/aaai07-355.php.
  19. Piero Andrea Bonatti, Stefan Decker, Axel Polleres, and Valentina Presutti. Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web (Dagstuhl Seminar 18371). Dagstuhl Reports, 8(9):29-111, 2018. URL: https://doi.org/10.4230/DAGREP.8.9.29.
  20. Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko. Translating Embeddings for Modeling Multi-relational Data. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States, pages 2787-2795, 2013. URL: https://proceedings.neurips.cc/paper/2013/hash/1cecc7a77928ca8133fa24680a88d2f9-Abstract.html.
  21. Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Velickovic. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges, 2021. URL: https://doi.org/10.48550/arXiv.2104.13478.
  22. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020. URL: https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html.
  23. Hongyun Cai, Vincent W. Zheng, and Kevin Chen-Chuan Chang. A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications. IEEE Transactions on Knowledge and Data Engineering, 30(9):1616-1637, 2018. URL: https://doi.org/10.1109/TKDE.2018.2807452.
  24. Yixin Cao, Xiang Ji, Xin Lv, Juanzi Li, Yonggang Wen, and Hanwang Zhang. Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, pages 6855-6865. Association for Computational Linguistics, 2021. URL: https://doi.org/10.18653/V1/2021.ACL-LONG.534.
  25. Shruthi Chari, Oshani Seneviratne, Mohamed Ghalwash, Sola Shirai, Daniel M. Gruen, Pablo Meyer, Prithwish Chakraborty, and Deborah L McGuinness. Explanation Ontology: A General-Purpose, Semantic Representation for Supporting User-Centered Explanations. Semantic Web, (to appear), 2023. URL: https://doi.org/10.3233/SW-233282.
  26. Michel Chein and Marie-Laure Mugnier. Graph-based Knowledge Representation - Computational Foundations of Conceptual Graphs. Advanced Information and Knowledge Processing. Springer, 2009. URL: https://doi.org/10.1007/978-1-84800-286-9.
  27. Jiaoyan Chen, Yuxia Geng, Zhuo Chen, Ian Horrocks, Jeff Z. Pan, and Huajun Chen. Knowledge-aware Zero-Shot Learning: Survey and Perspective. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021, pages 4366-4373. ijcai.org, 2021. URL: https://doi.org/10.24963/IJCAI.2021/597.
  28. Jiaoyan Chen, Yuxia Geng, Zhuo Chen, Jeff Z. Pan, Yuan He, Wen Zhang, Ian Horrocks, and Huajun Chen. Zero-Shot and Few-Shot Learning With Knowledge Graphs: A Comprehensive Survey. Proceedings of the IEEE, 111(6):653-685, 2023. URL: https://doi.org/10.1109/JPROC.2023.3279374.
  29. Xiaojun Chen, Shengbin Jia, and Yang Xiang. A review: Knowledge reasoning over knowledge graph. Expert Systems with Applications, 141, 2020. URL: https://doi.org/10.1016/J.ESWA.2019.112948.
  30. Zhuo Chen, Yufeng Huang, Jiaoyan Chen, Yuxia Geng, Yin Fang, Jeff Z. Pan, Ningyu Zhang, and Wen Zhang. LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection. In Proceedings of the 11th International Joint Conference on Knowledge Graphs, IJCKG 2022, Hangzhou, China, October 27-28, 2022, pages 20-29. ACM, 2022. URL: https://doi.org/10.1145/3579051.3579053.
  31. Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. Global RDF Vector Space Embeddings. In The Semantic Web - ISWC 2017 - 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part I, volume 10587 of Lecture Notes in Computer Science, pages 190-207. Springer, 2017. URL: https://doi.org/10.1007/978-3-319-68288-4_12.
  32. Roberto Confalonieri and Giancarlo Guizzardi. On the Multiple Roles of Ontologies in Explainable AI, 2023. URL: https://doi.org/10.48550/ARXIV.2311.04778.
  33. Claudia d'Amato. Machine Learning for the Semantic Web: Lessons learnt and next research directions. Semantic Web, 11(1):195-203, 2020. URL: https://doi.org/10.3233/SW-200388.
  34. Claudia d'Amato, Nicola Fanizzi, and Floriana Esposito. Query Answering and Ontology Population: An Inductive Approach. In The Semantic Web: Research and Applications, 5th European Semantic Web Conference, ESWC 2008, Tenerife, Canary Islands, Spain, June 1-5, 2008, Proceedings, volume 5021 of Lecture Notes in Computer Science, pages 288-302. Springer, 2008. URL: https://doi.org/10.1007/978-3-540-68234-9_23.
  35. Claudia d'Amato, Nicola Flavio Quatraro, and Nicola Fanizzi. Embedding Models for Knowledge Graphs Induced by Clusters of Relations and Background Knowledge. In Inductive Logic Programming - 30th International Conference, ILP 2021, Virtual Event, October 25-27, 2021, Proceedings, volume 13191 of Lecture Notes in Computer Science, pages 1-16. Springer, 2021. URL: https://doi.org/10.1007/978-3-030-97454-1_1.
  36. Claudia d'Amato, Nicola Flavio Quatraro, and Nicola Fanizzi. Injecting Background Knowledge into Embedding Models for Predictive Tasks on Knowledge Graphs. In The Semantic Web - 18th International Conference, ESWC 2021, Virtual Event, June 6-10, 2021, Proceedings, volume 12731 of Lecture Notes in Computer Science, pages 441-457. Springer, 2021. URL: https://doi.org/10.1007/978-3-030-77385-4_26.
  37. Claudia d'Amato, Andrea G. B. Tettamanzi, and Duc Minh Tran. Evolutionary Discovery of Multi-relational Association Rules from Ontological Knowledge Bases. In Knowledge Engineering and Knowledge Management - 20th International Conference, EKAW 2016, Bologna, Italy, November 19-23, 2016, Proceedings, volume 10024 of Lecture Notes in Computer Science, pages 113-128, 2016. URL: https://doi.org/10.1007/978-3-319-49004-5_8.
  38. Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja, and Ashwin Srinivasan. A review of some techniques for inclusion of domain-knowledge into deep neural networks. Scientific Reports, 12(1):1040, 2022. URL: https://doi.org/10.1038/s41598-021-04590-0.
  39. Jia Deng, Nan Ding, Yangqing Jia, Andrea Frome, Kevin Murphy, Samy Bengio, Yuan Li, Hartmut Neven, and Hartwig Adam. Large-Scale Object Classification Using Label Relation Graphs. In Computer Vision - ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I, volume 8689 of Lecture Notes in Computer Science, pages 48-64. Springer, 2014. URL: https://doi.org/10.1007/978-3-319-10590-1_4.
  40. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA, pages 248-255. IEEE Computer Society, 2009. URL: https://doi.org/10.1109/CVPR.2009.5206848.
  41. Jia Deng, Jonathan Krause, Alexander C. Berg, and Li Fei-Fei. Hedging your bets: Optimizing accuracy-specificity trade-offs in large scale visual recognition. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, June 16-21, 2012, pages 3450-3457. IEEE Computer Society, 2012. URL: https://doi.org/10.1109/CVPR.2012.6248086.
  42. Edmund Dervakos, Konstantinos Thomas, Giorgos Filandrianos, and Giorgos Stamou. Choose your data wisely: A framework for semantic counterfactuals. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI 2023, 19th-25th August 2023, Macao, SAR, China, pages 382-390. ijcai.org, 2023. URL: https://doi.org/10.24963/IJCAI.2023/43.
  43. Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. Convolutional 2D Knowledge Graph Embeddings. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, pages 1811-1818. AAAI Press, 2018. URL: https://doi.org/10.1609/AAAI.V32I1.11573.
  44. Xin Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Ni Lao, Kevin Murphy, Thomas Strohmann, Shaohua Sun, and Wei Zhang. Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14, New York, NY, USA - August 24 - 27, 2014, pages 601-610. ACM, 2014. URL: https://doi.org/10.1145/2623330.2623623.
  45. Alaaeldin El-Nouby, Shikhar Sharma, Hannes Schulz, R. Devon Hjelm, Layla El Asri, Samira Ebrahimi Kahou, Yoshua Bengio, and Graham W. Taylor. Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction. In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, pages 10303-10311. IEEE, 2019. URL: https://doi.org/10.1109/ICCV.2019.01040.
  46. Nicola Fanizzi, Claudia d'Amato, and Floriana Esposito. DL-FOIL Concept Learning in Description Logics. In Inductive Logic Programming, 18th International Conference, ILP 2008, Prague, Czech Republic, September 10-12, 2008, Proceedings, volume 5194 of Lecture Notes in Computer Science, pages 107-121. Springer, 2008. URL: https://doi.org/10.1007/978-3-540-85928-4_12.
  47. Nicola Fanizzi, Claudia d'Amato, and Floriana Esposito. Metric-based stochastic conceptual clustering for ontologies. Information Systems, 34(8):792-806, 2009. URL: https://doi.org/10.1016/J.IS.2009.03.008.
  48. Shangbin Feng, Vidhisha Balachandran, Yuyang Bai, and Yulia Tsvetkov. FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge, 2023. URL: https://doi.org/10.48550/ARXIV.2305.08281.
  49. Yanwei Fu, Tao Xiang, Yu-Gang Jiang, Xiangyang Xue, Leonid Sigal, and Shaogang Gong. Recent Advances in Zero-Shot Recognition: Toward Data-Efficient Understanding of Visual Content. IEEE Signal Processing Magazine, 35(1):112-125, 2018. URL: https://doi.org/10.1109/MSP.2017.2763441.
  50. Luis Galárraga, Christina Teflioudi, Katja Hose, and Fabian M. Suchanek. Fast rule mining in ontological knowledge bases with AMIE+. The VLDB Journal, 24(6):707-730, 2015. URL: https://doi.org/10.1007/S00778-015-0394-1.
  51. Luis Antonio Galárraga, Christina Teflioudi, Katja Hose, and Fabian M. Suchanek. AMIE: association rule mining under incomplete evidence in ontological knowledge bases. In 22nd International World Wide Web Conference, WWW '13, Rio de Janeiro, Brazil, May 13-17, 2013, pages 413-422. International World Wide Web Conferences Steering Committee / ACM, 2013. URL: https://doi.org/10.1145/2488388.2488425.
  52. Yang Gao, Yi-Fan Li, Yu Lin, Hang Gao, and Latifur Khan. Deep Learning on Knowledge Graph for Recommender System: A Survey, 2020. URL: https://doi.org/10.48550/arXiv.2004.00387.
  53. Yuxia Geng, Jiaoyan Chen, Zhiquan Ye, Zonggang Yuan, Wei Zhang, and Huajun Chen. Explainable zero-shot learning via attentive graph convolutional network and knowledge graphs. Semantic Web, 12(5):741-765, 2021. URL: https://doi.org/10.3233/SW-210435.
  54. Lise Getoor and Ben Taskar, editors. Introduction to Statistical Relational Learning. MIT Press, 2007. Google Scholar
  55. Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael A. Specter, and Lalana Kagal. Explaining Explanations: An Overview of Interpretability of Machine Learning. In 5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018, Turin, Italy, October 1-3, 2018, pages 80-89. IEEE, 2018. URL: https://doi.org/10.1109/DSAA.2018.00018.
  56. Olga Golovneva, Moya Chen, Spencer Poff, Martin Corredor, Luke Zettlemoyer, Maryam Fazel-Zarandi, and Asli Celikyilmaz. ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. URL: https://openreview.net/pdf?id=xYlJRpzZtsY.
  57. Ramanathan V. Guha. Towards A Model Theory for Distributed Representations. In 2015 AAAI Spring Symposia, Stanford University, Palo Alto, California, USA, March 22-25, 2015. AAAI Press, 2015. URL: http://www.aaai.org/ocs/index.php/SSS/SSS15/paper/view/10220.
  58. Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. A Survey of Methods for Explaining Black Box Models. ACM Computing Surveys, 51(5):93:1-93:42, 2019. URL: https://doi.org/10.1145/3236009.
  59. Lin Guo and Qun Dai. Graph Clustering via Variational Graph Embedding. Pattern Recognition, 122:108334, 2022. URL: https://doi.org/10.1016/J.PATCOG.2021.108334.
  60. Shu Guo, Quan Wang, Lihong Wang, Bin Wang, and Li Guo. Jointly Embedding Knowledge Graphs and Logical Rules. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1-4, 2016, pages 192-202. The Association for Computational Linguistics, 2016. URL: https://doi.org/10.18653/V1/D16-1019.
  61. Wenzhong Guo, Jianwen Wang, and Shiping Wang. Deep Multimodal Representation Learning: A Survey. IEEE Access, 7:63373-63394, 2019. URL: https://doi.org/10.1109/ACCESS.2019.2916887.
  62. Víctor Gutiérrez-Basulto and Steven Schockaert. From Knowledge Graph Embedding to Ontology Embedding? An Analysis of the Compatibility between Vector Space Representations and Rules. In Principles of Knowledge Representation and Reasoning: Proceedings of the Sixteenth International Conference, KR 2018, Tempe, Arizona, 30 October - 2 November 2018, pages 379-388. AAAI Press, 2018. URL: https://aaai.org/ocs/index.php/KR/KR18/paper/view/18013.
  63. Ayoub Harnoune, Maryem Rhanoui, Mounia Mikram, Siham Yousfi, Zineb Elkaimbillah, and Bouchra El Asri. BERT based clinical knowledge extraction for biomedical knowledge graph construction and analysis. Computer Methods and Programs in Biomedicine Update, 1:100042, 2021. URL: https://doi.org/10.1016/j.cmpbup.2021.100042.
  64. Frederick Hayes-Roth, Donald A Waterman, and Douglas B Lenat. Building expert systems. Addison-Wesley Longman Publishing Co., Inc., 1983. Google Scholar
  65. Bin He, Daoyuan Zhou, Jingjing Xiao, Xiangyang Jiang, Qun Liu, Nianwen J. Yuan, and Tao Xu. BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2281-2290, Online, nov 2020. Association for Computational Linguistics. URL: https://doi.org/10.18653/v1/2020.findings-emnlp.207.
  66. Feijuan He, Yaxian Wang, Xianglin Miao, and Xia Sun. Interpretable visual reasoning: A survey. Image and Vision Computing, 112:104194, 2021. URL: https://doi.org/10.1016/J.IMAVIS.2021.104194.
  67. Shizhu He, Kang Liu, Guoliang Ji, and Jun Zhao. Learning to Represent Knowledge Graphs with Gaussian Embedding. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourne, VIC, Australia, October 19 - 23, 2015, pages 623-632. ACM, 2015. URL: https://doi.org/10.1145/2806416.2806502.
  68. Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, and Antoine Zimmermann. Knowledge Graphs. Synthesis Lectures on Data, Semantics, and Knowledge. Morgan & Claypool Publishers, 2021. URL: https://doi.org/10.2200/S01125ED1V01Y202109DSK022.
  69. Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d’Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, et al. Knowledge graphs. ACM Computing Surveys, 54(4):1-37, 2021. URL: https://doi.org/10.1145/3447772.
  70. Ian Horrocks, Peter. F. Patel-Schneider, Harold Boley, Said Tabet, Benjamin Grosof, and Mike Dean. SWRL: A semantic web rule language combining OWL and RuleML, 2004. URL: http://www.daml.org/2004/04/swrl/rules-all.html.
  71. Yang Hu, Adriane Chapman, Guihua Wen, and Wendy Hall. What Can Knowledge Bring to Machine Learning? - A Survey of Low-shot Learning for Structured Data. ACM Transactions on Intelligent Systems and Technology, 13(3):48:1-48:45, 2022. URL: https://doi.org/10.1145/3510030.
  72. Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang, Xiaoli Li, Ru Li, and Jeff Z. Pan. Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, pages 3078-3084. ijcai.org, 2022. URL: https://doi.org/10.24963/IJCAI.2022/427.
  73. Xiao Huang, Jingyuan Zhang, Dingcheng Li, and Ping Li. Knowledge graph embedding based question answering. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, February 11-15, 2019, pages 105-113. ACM, 2019. URL: https://doi.org/10.1145/3289600.3290956.
  74. Zhicheng Huang, Zhaoyang Zeng, Yupan Huang, Bei Liu, Dongmei Fu, and Jianlong Fu. Seeing Out of the Box: End-to-End Pre-Training for Vision-Language Representation Learning. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021, pages 12976-12985. Computer Vision Foundation / IEEE, 2021. URL: https://doi.org/10.1109/CVPR46437.2021.01278.
  75. Nicolas Hubert, Pierre Monnin, Armelle Brun, and Davy Monticolo. Knowledge Graph Embeddings for Link Prediction: Beware of Semantics! In Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG 2022) co-located with the 21th International Semantic Web Conference (ISWC 2022), Virtual Conference, online, October 24, 2022, volume 3342 of CEUR Workshop Proceedings. CEUR-WS.org, 2022. URL: https://ceur-ws.org/Vol-3342/paper-4.pdf.
  76. Nicolas Hubert, Pierre Monnin, Armelle Brun, and Davy Monticolo. New Strategies for Learning Knowledge Graph Embeddings: The Recommendation Case. In Knowledge Engineering and Knowledge Management - 23rd International Conference, EKAW 2022, Bolzano, Italy, September 26-29, 2022, Proceedings, volume 13514 of Lecture Notes in Computer Science, pages 66-80. Springer, 2022. URL: https://doi.org/10.1007/978-3-031-17105-5_5.
  77. Nicolas Hubert, Pierre Monnin, Armelle Brun, and Davy Monticolo. Sem@K: Is my knowledge graph embedding model semantic-aware? Semantic Web, (to appear), 2023. URL: https://doi.org/10.48550/arXiv.2301.05601.
  78. Nicolas Hubert, Pierre Monnin, Armelle Brun, and Davy Monticolo. Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction, 2023. URL: https://doi.org/10.48550/arXiv.2303.00286.
  79. Andreea Iana and Heiko Paulheim. More is not Always Better: The Negative Impact of A-box Materialization on RDF2vec Knowledge Graph Embeddings. In 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, volume 2699 of CEUR Workshop Proceedings. CEUR-WS.org, 2020. URL: https://ceur-ws.org/Vol-2699/paper05.pdf.
  80. Nitisha Jain, Jan-Christoph Kalo, Wolf-Tilo Balke, and Ralf Krestel. Do Embeddings Actually Capture Knowledge Graph Semantics? In The Semantic Web - 18th International Conference, ESWC 2021, Virtual Event, June 6-10, 2021, Proceedings, volume 12731 of Lecture Notes in Computer Science, pages 143-159. Springer, 2021. URL: https://doi.org/10.1007/978-3-030-77385-4_9.
  81. Lucas Jarnac, Miguel Couceiro, and Pierre Monnin. Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023, pages 934-944. ACM, 2023. URL: https://doi.org/10.1145/3583780.3615030.
  82. Mirantha Jayathilaka, Tingting Mu, and Uli Sattler. Visual-Semantic Embedding Model Informed by Structured Knowledge. In Proceedings of the 9th European Starting AI Researchers' Symposium 2020 co-located with 24th European Conference on Artificial Intelligence (ECAI 2020), Santiago Compostela, Spain, August, 2020, volume 2655 of CEUR Workshop Proceedings. CEUR-WS.org, 2020. URL: https://ceur-ws.org/Vol-2655/paper23.pdf.
  83. Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and Philip S. Yu. A Survey on Knowledge Graphs: Representation, Acquisition, and Applications. IEEE Transactions on Neural Networks and Learning Systems, 33(2):494-514, 2022. URL: https://doi.org/10.1109/TNNLS.2021.3070843.
  84. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, and Pascale Fung. Survey of Hallucination in Natural Language Generation. ACM Computing Surveys, 55(12):248:1-248:38, 2023. URL: https://doi.org/10.1145/3571730.
  85. Ziwei Ji, Zihan Liu, Nayeon Lee, Tiezheng Yu, Bryan Wilie, Min Zeng, and Pascale Fung. RHO: Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding. In Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada, July 9-14, 2023, pages 4504-4522. Association for Computational Linguistics, 2023. URL: https://doi.org/10.18653/V1/2023.FINDINGS-ACL.275.
  86. J. Józefowska, A Lawrynowicz, and T. Lukaszewski. The role of semantics in mining frequent patterns from knowledge bases in description logics with rules. Theory and Practice of Logic Programming, 10(3):251-289, 2010. URL: https://doi.org/10.1017/S1471068410000098.
  87. Serhiy Kandul, Vincent Micheli, Juliane Beck, Markus Kneer, Thomas Burri, François Fleuret, and Markus Christen. Explainable AI: A Review of the Empirical Literature. SSRN 4325219, 2023. URL: https://doi.org/10.2139/ssrn.4325219.
  88. C. Maria Keet, Agnieszka Lawrynowicz, Claudia d'Amato, Alexandros Kalousis, Phong Nguyen, Raúl Palma, Robert Stevens, and Melanie Hilario. The Data Mining OPtimization Ontology. Journal of Web Semantics, 32:43-53, 2015. URL: https://doi.org/10.1016/J.WEBSEM.2015.01.001.
  89. Mayank Kejriwal. Domain-Specific Knowledge Graph Construction. Springer Briefs in Computer Science. Springer, 2019. URL: https://doi.org/10.1007/978-3-030-12375-8.
  90. Wonjae Kim, Bokyung Son, and Ildoo Kim. ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 5583-5594. PMLR, 2021. URL: http://proceedings.mlr.press/v139/kim21k.html.
  91. Daphne Koller and Nir Friedman, editors. Probabilistic graphical models: principles and techniques. MIT Press, 2009. URL: http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=11886.
  92. Wei Kun Kong, Xin Liu, Teeradaj Racharak, Guanqun Sun, Jianan Chen, Qiang Ma, and Le-Minh Nguyen. WeExt: A Framework of Extending Deterministic Knowledge Graph Embedding Models for Embedding Weighted Knowledge Graphs. IEEE Access, 11:48901-48911, 2023. URL: https://doi.org/10.1109/ACCESS.2023.3276319.
  93. Lili Kotlerman, Ido Dagan, Bernardo Magnini, and Luisa Bentivogli. Textual entailment graphs. Natural Language Engineering, 21(5):699-724, 2015. URL: https://doi.org/10.1017/S1351324915000108.
  94. Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A. Shamma, Michael S. Bernstein, and Li Fei-Fei. Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations. International Journal of Computer Vision, 123(1):32-73, 2017. URL: https://doi.org/10.1007/S11263-016-0981-7.
  95. Anastasia Kritharoula, Maria Lymperaiou, and Giorgos Stamou. Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation, 2023. URL: https://doi.org/10.48550/ARXIV.2310.14025.
  96. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6):84-90, 2017. URL: https://doi.org/10.1145/3065386.
  97. Denis Krompaß, Stephan Baier, and Volker Tresp. Type-Constrained Representation Learning in Knowledge Graphs. In The Semantic Web - ISWC 2015 - 14th International Semantic Web Conference, Bethlehem, PA, USA, October 11-15, 2015, Proceedings, Part I, volume 9366 of Lecture Notes in Computer Science, pages 640-655. Springer, 2015. URL: https://doi.org/10.1007/978-3-319-25007-6_37.
  98. Abhijeet Kumar, Abhishek Pandey, Rohit Gadia, and Mridul Mishra. Building knowledge graph using pre-trained language model for learning entity-aware relationships. In 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON), pages 310-315. IEEE, 2020. URL: https://doi.org/10.1109/GUCON48875.2020.9231227.
  99. Ugur Kursuncu, Manas Gaur, and Amit P. Sheth. Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning. In 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, volume 2600 of CEUR Workshop Proceedings. CEUR-WS.org, 2020. URL: https://ceur-ws.org/Vol-2600/paper14.pdf.
  100. Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling. Learning to detect unseen object classes by between-class attribute transfer. In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA, pages 951-958. IEEE Computer Society, 2009. URL: https://doi.org/10.1109/CVPR.2009.5206594.
  101. Philippe Langlais, François Yvon, and Pierre Zweigenbaum. Improvements in Analogical Learning: Application to Translating Multi-Terms of the Medical Domain. In EACL 2009, 12th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, Athens, Greece, March 30 - April 3, 2009, pages 487-495. The Association for Computer Linguistics, 2009. URL: https://aclanthology.org/E09-1056/.
  102. Jens Lehmann, Sören Auer, Lorenz Bühmann, and Sebastian Tramp. Class expression learning for ontology engineering. Journal of Web Semantics, 9(1):71-81, 2011. URL: https://doi.org/10.1016/J.WEBSEM.2011.01.001.
  103. Jens Lehmann and Lorenz Bühmann. ORE - A Tool for Repairing and Enriching Knowledge Bases. In The Semantic Web - ISWC 2010 - 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part II, volume 6497 of Lecture Notes in Computer Science, pages 177-193. Springer, 2010. URL: https://doi.org/10.1007/978-3-642-17749-1_12.
  104. Douglas B. Lenat, Alan Borning, David W. McDonald, Craig Taylor, and Steven Weyer. Knoesphere: Building Expert Systems With Encyclopedic Knowledge. In Proceedings of the 8th International Joint Conference on Artificial Intelligence. Karlsruhe, FRG, August 1983, pages 167-169. William Kaufmann, 1983. URL: http://ijcai.org/Proceedings/83-1/Papers/034.pdf.
  105. Adam Lerer, Ledell Wu, Jiajun Shen, Timothée Lacroix, Luca Wehrstedt, Abhijit Bose, and Alex Peysakhovich. Pytorch-BigGraph: A Large Scale Graph Embedding System. In Proceedings of Machine Learning and Systems 2019, MLSys 2019, Stanford, CA, USA, March 31 - April 2, 2019. mlsys.org, 2019. URL: https://proceedings.mlsys.org/book/282.pdf.
  106. Paul Lerner, Olivier Ferret, and Camille Guinaudeau. Multimodal Inverse Cloze Task for Knowledge-Based Visual Question Answering. In Advances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2-6, 2023, Proceedings, Part I, volume 13980 of Lecture Notes in Computer Science, pages 569-587. Springer, 2023. URL: https://doi.org/10.1007/978-3-031-28244-7_36.
  107. Guohao Li, Xin Wang, and Wenwu Zhu. Boosting Visual Question Answering with Context-aware Knowledge Aggregation. In MM '20: The 28th ACM International Conference on Multimedia, Virtual Event / Seattle, WA, USA, October 12-16, 2020, pages 1227-1235. ACM, 2020. URL: https://doi.org/10.1145/3394171.3413943.
  108. Shaobo Li, Xiaoguang Li, Lifeng Shang, Zhenhua Dong, Chengjie Sun, Bingquan Liu, Zhenzhou Ji, Xin Jiang, and Qun Liu. How Pre-trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysis. In Findings of the Association for Computational Linguistics: ACL 2022, Dublin, Ireland, May 22-27, 2022, pages 1720-1732. Association for Computational Linguistics, 2022. URL: https://doi.org/10.18653/V1/2022.FINDINGS-ACL.136.
  109. Shaobo Li, Xiaoguang Li, Lifeng Shang, Chengjie Sun, Bingquan Liu, Zhenzhou Ji, Xin Jiang, and Qun Liu. Pre-training Language Models with Deterministic Factual Knowledge. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, pages 11118-11131. Association for Computational Linguistics, 2022. URL: https://doi.org/10.18653/V1/2022.EMNLP-MAIN.764.
  110. Xuhong Li, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Xiao Zhang, Ji Liu, Jiang Bian, and Dejing Dou. Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond. Knowledge and Information Systems, 64(12):3197-3234, 2022. URL: https://doi.org/10.1007/S10115-022-01756-8.
  111. Jason Liartis, Edmund Dervakos, Orfeas Menis-Mastromichalakis, Alexandros Chortaras, and Giorgos Stamou. Searching for explanations of black-box classifiers in the space of semantic queries. Semantic Web, (to appear), 2023. URL: https://doi.org/10.3233/SW-233469.
  112. Yankai Lin, Xu Han, Ruobing Xie, Zhiyuan Liu, and Maosong Sun. Knowledge Representation Learning: A Quantitative Review, 2018. URL: https://doi.org/10.48550/arXiv.1812.10901.
  113. Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. Learning Entity and Relation Embeddings for Knowledge Graph Completion. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, USA, pages 2181-2187. AAAI Press, 2015. URL: https://doi.org/10.1609/AAAI.V29I1.9491.
  114. Pantelis Linardatos, Vasilis Papastefanopoulos, and Sotiris Kotsiantis. Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy, 23(1):18, 2021. URL: https://doi.org/10.3390/E23010018.
  115. Hanxiao Liu, Yuexin Wu, and Yiming Yang. Analogical Inference for Multi-relational Embeddings. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, volume 70 of Proceedings of Machine Learning Research, pages 2168-2178. PMLR, 2017. URL: http://proceedings.mlr.press/v70/liu17d.html.
  116. Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, and Hannaneh Hajishirzi. Generated Knowledge Prompting for Commonsense Reasoning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, pages 3154-3169. Association for Computational Linguistics, 2022. URL: https://doi.org/10.18653/V1/2022.ACL-LONG.225.
  117. Shuwen Liu, Bernardo Cuenca Grau, Ian Horrocks, and Egor V. Kostylev. Revisiting Inferential Benchmarks for Knowledge Graph Completion. In Proceedings of the 20th International Conference on Principles of Knowledge Representation and Reasoning, KR 2023, Rhodes, Greece, September 2-8, 2023, pages 461-471, 2023. URL: https://doi.org/10.24963/KR.2023/45.
  118. Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, and Ping Wang. K-BERT: Enabling Language Representation with Knowledge Graph. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pages 2901-2908. AAAI Press, 2020. URL: https://doi.org/10.1609/AAAI.V34I03.5681.
  119. Maria Lymperaiou and Giorgos Stamou. The Contribution of Knowledge in Visiolinguistic Learning: A Survey on Tasks and Challenges. In Proceedings of the AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering (AAAI-MAKE 2023), Hyatt Regency, San Francisco Airport, California, USA, March 27-29, 2023, volume 3433 of CEUR Workshop Proceedings. CEUR-WS.org, 2023. URL: https://ceur-ws.org/Vol-3433/paper18.pdf.
  120. Louis Mahon, Eleonora Giunchiglia, Bowen Li, and Thomas Lukasiewicz. Knowledge Graph Extraction from Videos. In 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020, Miami, FL, USA, December 14-17, 2020, pages 25-32. IEEE, 2020. URL: https://doi.org/10.1109/ICMLA51294.2020.00014.
  121. Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023, pages 9802-9822. Association for Computational Linguistics, 2023. URL: https://doi.org/10.18653/V1/2023.ACL-LONG.546.
  122. Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi. OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pages 3195-3204. Computer Vision Foundation / IEEE, 2019. URL: https://doi.org/10.1109/CVPR.2019.00331.
  123. Christian Meilicke, Melisachew Wudage Chekol, Manuel Fink Patrick Betz, and Heiner Stuckeschmidt. Anytime bottom-up rule learning for large-scale knowledge graph completion. The VLDB Journal, 2023. URL: https://doi.org/10.1007/s00778-023-00800-5.
  124. André Melo and Heiko Paulheim. Synthesizing Knowledge Graphs for Link and Type Prediction Benchmarking. In The Semantic Web - 14th International Conference, ESWC 2017, Portorož, Slovenia, May 28 - June 1, 2017, Proceedings, Part I, volume 10249 of Lecture Notes in Computer Science, pages 136-151, 2017. URL: https://doi.org/10.1007/978-3-319-58068-5_9.
  125. Zaiqiao Meng, Fangyu Liu, Ehsan Shareghi, Yixuan Su, Charlotte Collins, and Nigel Collier. Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, pages 4798-4810. Association for Computational Linguistics, 2022. URL: https://doi.org/10.18653/V1/2022.ACL-LONG.329.
  126. Laurent Miclet, Sabri Bayoudh, and Arnaud Delhay. Analogical Dissimilarity: Definition, Algorithms and Two Experiments in Machine Learning. Journal of Artificial Intelligence Research, 32:793-824, 2008. URL: https://doi.org/10.1613/JAIR.2519.
  127. George A. Miller. WordNet: A Lexical Database for English. Communications of the ACM, 38(11):39-41, 1995. URL: https://doi.org/10.1145/219717.219748.
  128. Pasquale Minervini, Luca Costabello, Emir Muñoz, Vít Novácek, and Pierre-Yves Vandenbussche. Regularizing Knowledge Graph Embeddings via Equivalence and Inversion Axioms. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part I, volume 10534 of Lecture Notes in Computer Science, pages 668-683. Springer, 2017. URL: https://doi.org/10.1007/978-3-319-71249-9_40.
  129. Pasquale Minervini, Thomas Demeester, Tim Rocktäschel, and Sebastian Riedel. Adversarial Sets for Regularising Neural Link Predictors. In Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, UAI 2017, Sydney, Australia, August 11-15, 2017. AUAI Press, 2017. URL: http://auai.org/uai2017/proceedings/papers/306.pdf.
  130. Riccardo Miotto, Fei Wang, Shuang Wang, Xiaoqian Jiang, and Joel T Dudley. Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics, 19(6):1236-1246, 2018. URL: https://doi.org/10.1093/BIB/BBX044.
  131. Melanie Mitchell. Abstraction and Analogy-Making in Artificial Intelligence. Annals of the New York Academy of Sciences, 1505(1):79-101, 2021. URL: https://doi.org/10.1111/nyas.14619.
  132. Aditya Mogadala, Marimuthu Kalimuthu, and Dietrich Klakow. Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods. Journal of Artificial Intelligence Research, 71:1183-1317, 2021. URL: https://doi.org/10.1613/JAIR.1.11688.
  133. Sebastian Monka, Lavdim Halilaj, and Achim Rettinger. A survey on visual transfer learning using knowledge graphs. Semantic Web, 13(3):477-510, 2022. URL: https://doi.org/10.3233/SW-212959.
  134. Pierre Monnin and Miguel Couceiro. Interactions Between Knowledge Graph-Related Tasks and Analogical Reasoning: A Discussion. In Workshop Proceedings of the 30th International Conferece on Case-Based Reasoning co-located with the 30th International Conference on Case-Based Reasoning (ICCBR 2022), Nancy (France), September 12-15th, 2022, volume 3389 of CEUR Workshop Proceedings, pages 57-67. CEUR-WS.org, 2022. URL: https://ceur-ws.org/Vol-3389/ICCBR_2022_Workshop_paper_75.pdf.
  135. Diego Moussallem, Mihael Arcan, Axel-Cyrille Ngonga Ngomo, and Paul Buitelaar. Augmenting Neural Machine Translation with Knowledge Graphs, 2019. https://arxiv.org/abs/1902.08816, URL: https://doi.org/10.48550/arXiv.1902.08816.
  136. Daniel Neil, Joss Briody, Alix Lacoste, Aaron Sim, Páidí Creed, and Amir Saffari. Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs, 2018. URL: https://doi.org/10.48550/arXiv.1812.00279.
  137. Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, and Dinh Q. Phung. A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 2 (Short Papers), pages 327-333. Association for Computational Linguistics, 2018. URL: https://doi.org/10.18653/V1/N18-2053.
  138. Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich. A Review of Relational Machine Learning for Knowledge Graphs. Proceedings of the IEEE, 104(1):11-33, 2016. URL: https://doi.org/10.1109/JPROC.2015.2483592.
  139. Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. A Three-Way Model for Collective Learning on Multi-Relational Data. In Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011, pages 809-816. Omnipress, 2011. URL: https://icml.cc/2011/papers/438_icmlpaper.pdf.
  140. Vicente Ordonez, Jia Deng, Yejin Choi, Alexander C. Berg, and Tamara L. Berg. From Large Scale Image Categorization to Entry-Level Categories. In IEEE International Conference on Computer Vision, ICCV 2013, Sydney, Australia, December 1-8, 2013, pages 2768-2775. IEEE Computer Society, 2013. URL: https://doi.org/10.1109/ICCV.2013.344.
  141. Mark Palatucci, Dean Pomerleau, Geoffrey E. Hinton, and Tom M. Mitchell. Zero-shot Learning with Semantic Output Codes. In Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009., pages 1410-1418. Curran Associates, Inc., 2009. URL: https://proceedings.neurips.cc/paper/2009/hash/1543843a4723ed2ab08e18053ae6dc5b-Abstract.html.
  142. Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. Unifying Large Language Models and Knowledge Graphs: A Roadmap, 2023. URL: https://doi.org/10.48550/ARXIV.2306.08302.
  143. Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, and Christos Faloutsos. Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019, pages 596-606. ACM, 2019. URL: https://doi.org/10.1145/3292500.3330855.
  144. Heiko Paulheim. Make Embeddings Semantic Again! In Proceedings of the ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks co-located with 17th International Semantic Web Conference (ISWC 2018), Monterey, USA, October 8th - to - 12th, 2018, volume 2180 of CEUR Workshop Proceedings. CEUR-WS.org, 2018. URL: https://ceur-ws.org/Vol-2180/ISWC_2018_Outrageous_Ideas_paper_4.pdf.
  145. Ciyuan Peng, Feng Xia, Mehdi Naseriparsa, and Francesco Osborne. Knowledge Graphs: Opportunities and Challenges. Artificial Intelligence Review, 56(11):13071-13102, 2023. URL: https://doi.org/10.1007/S10462-023-10465-9.
  146. Matthew E. Peters, Mark Neumann, Robert L. Logan IV, Roy Schwartz, Vidur Joshi, Sameer Singh, and Noah A. Smith. Knowledge Enhanced Contextual Word Representations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, pages 43-54. Association for Computational Linguistics, 2019. URL: https://doi.org/10.18653/V1/D19-1005.
  147. Jan Portisch, Nicolas Heist, and Heiko Paulheim. Knowledge graph embedding for data mining vs. knowledge graph embedding for link prediction - two sides of the same coin? Semantic Web, 13(3):399-422, 2022. URL: https://doi.org/10.3233/SW-212892.
  148. Jan Portisch and Heiko Paulheim. The DLCC Node Classification Benchmark for Analyzing Knowledge Graph Embeddings. In The Semantic Web - ISWC 2022 - 21st International Semantic Web Conference, Virtual Event, October 23-27, 2022, Proceedings, volume 13489 of Lecture Notes in Computer Science, pages 592-609. Springer, 2022. URL: https://doi.org/10.1007/978-3-031-19433-7_34.
  149. Chen Qu, Hamed Zamani, Liu Yang, W. Bruce Croft, and Erik G. Learned-Miller. Passage Retrieval for Outside-Knowledge Visual Question Answering. In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021, pages 1753-1757. ACM, 2021. URL: https://doi.org/10.1145/3404835.3462987.
  150. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning Transferable Visual Models From Natural Language Supervision. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 8748-8763. PMLR, 2021. URL: http://proceedings.mlr.press/v139/radford21a.html.
  151. Luc De Raedt, editor. Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies). Springer-Verlag, 2008. URL: https://doi.org/10.1007/978-3-540-68856-3.
  152. Enayat Rajabi and Kobra Etminani. Knowledge-graph-based explainable AI: A systematic review. Journal of Information Science, 2022. URL: https://doi.org/10.1177/016555152211128.
  153. Achim Rettinger, Matthias Nickles, and Volker Tresp. Statistical Relational Learning with Formal Ontologies. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part II, volume 5782 of Lecture Notes in Computer Science, pages 286-301. Springer, 2009. URL: https://doi.org/10.1007/978-3-642-04174-7_19.
  154. P. Ristoski and H. Paulheim. RDF2Vec: RDF Graph Embeddings for Data Mining. In The Semantic Web - ISWC 2016 - 15th International Semantic Web Conference, Proceedings, Part I, volume 9981 of LNCS, pages 498-514. Springer, 2016. URL: https://doi.org/10.1007/978-3-319-46523-4_30.
  155. Petar Ristoski, Gerben Klaas Dirk de Vries, and Heiko Paulheim. A Collection of Benchmark Datasets for Systematic Evaluations of Machine Learning on the Semantic Web. In The Semantic Web - ISWC 2016 - 15th International Semantic Web Conference, Kobe, Japan, October 17-21, 2016, Proceedings, Part II, volume 9982 of Lecture Notes in Computer Science, pages 186-194, 2016. URL: https://doi.org/10.1007/978-3-319-46547-0_20.
  156. Giuseppe Rizzo, Claudia d'Amato, and Nicola Fanizzi. An unsupervised approach to disjointness learning based on terminological cluster trees. Semantic Web, 12(3):423-447, 2021. URL: https://doi.org/10.3233/SW-200391.
  157. Giuseppe Rizzo, Claudia d'Amato, Nicola Fanizzi, and Floriana Esposito. Terminological Cluster Trees for Disjointness Axiom Discovery. In The Semantic Web - 14th International Conference, ESWC 2017, Portorož, Slovenia, May 28 - June 1, 2017, Proceedings, Part I, volume 10249 of Lecture Notes in Computer Science, pages 184-201, 2017. URL: https://doi.org/10.1007/978-3-319-58068-5_12.
  158. Giuseppe Rizzo, Nicola Fanizzi, and Claudia d'Amato. Class expression induction as concept space exploration: From DL-Foil to DL-Focl. Future Generation Computing Systems, 108:256-272, 2020. URL: https://doi.org/10.1016/J.FUTURE.2020.02.071.
  159. Giuseppe Rizzo, Nicola Fanizzi, Claudia d'Amato, and Floriana Esposito. A Framework for Tackling Myopia in Concept Learning on the Web of Data. In Knowledge Engineering and Knowledge Management - 21st International Conference, EKAW 2018, Nancy, France, November 12-16, 2018, Proceedings, volume 11313 of Lecture Notes in Computer Science, pages 338-354. Springer, 2018. URL: https://doi.org/10.1007/978-3-030-03667-6_22.
  160. Natalia Díaz Rodríguez, Alberto Lamas, Jules Sanchez, Gianni Franchi, Ivan Donadello, Siham Tabik, David Filliat, Policarpo Cruz, Rosana Montes, and Francisco Herrera. EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: The MonuMAI cultural heritage use case. Information Fusion, 79:58-83, 2022. URL: https://doi.org/10.1016/J.INFFUS.2021.09.022.
  161. Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach (4th Edition). Pearson, 2020. URL: http://aima.cs.berkeley.edu/.
  162. Babak Shahbaba and Radford M. Neal. Improving Classification When a Class Hierarchy is Available Using a Hierarchy-Based Prior, 2005. URL: https://doi.org/10.48550/arXiv.math/0510449.
  163. Amit P. Sheth, Manas Gaur, Ugur Kursuncu, and Ruwan Wickramarachchi. Shades of Knowledge-Infused Learning for Enhancing Deep Learning. IEEE Internet Computing, 23(6):54-63, 2019. URL: https://doi.org/10.1109/MIC.2019.2960071.
  164. Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pages 4222-4235. Association for Computational Linguistics, 2020. URL: https://doi.org/10.18653/V1/2020.EMNLP-MAIN.346.
  165. Prashant Shiralkar, Alessandro Flammini, Filippo Menczer, and Giovanni Luca Ciampaglia. Finding Streams in Knowledge Graphs to Support Fact Checking. In 2017 IEEE International Conference on Data Mining, ICDM 2017, New Orleans, LA, USA, November 18-21, 2017, pages 859-864. IEEE Computer Society, 2017. URL: https://doi.org/10.1109/ICDM.2017.105.
  166. Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan L. Boyd-Graber, and Lijuan Wang. Prompting GPT-3 To Be Reliable. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. URL: https://openreview.net/pdf?id=98p5x51L5af.
  167. Vivian Dos Santos Silva, André Freitas, and Siegfried Handschuh. Exploring Knowledge Graphs in an Interpretable Composite Approach for Text Entailment. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, pages 7023-7030. AAAI Press, 2019. URL: https://doi.org/10.1609/AAAI.V33I01.33017023.
  168. Fernando Sola, Daniel Ayala, Rafael Ayala, Inma Hernández, Carlos R. Rivero, and David Ruiz. AYNEXT - tools for streamlining the evaluation of link prediction techniques. SoftwareX, 23:101474, 2023. URL: https://doi.org/10.1016/J.SOFTX.2023.101474.
  169. Matteo Stefanini, Marcella Cornia, Lorenzo Baraldi, Silvia Cascianelli, Giuseppe Fiameni, and Rita Cucchiara. From Show to Tell: A Survey on Deep Learning-Based Image Captioning. IEEE Transactions on Patterns Analysis and Machine Intelligence, 45(1):539-559, 2023. URL: https://doi.org/10.1109/TPAMI.2022.3148210.
  170. Fenglong Su, Chengjin Xu, Han Yang, Zhongwu Chen, and Ning Jing. Neural entity alignment with cross-modal supervision. Information Processing and Management, 60(2):103174, 2023. URL: https://doi.org/10.1016/J.IPM.2022.103174.
  171. Yan Su, Xu Han, Zhiyuan Zhang, Yankai Lin, Peng Li, Zhiyuan Liu, Jie Zhou, and Maosong Sun. CokeBERT: Contextual Knowledge Selection and Embedding towards Enhanced Pre-trained Language Models. AI Open, 2:127-134, 2021. URL: https://doi.org/10.1016/J.AIOPEN.2021.06.004.
  172. Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. Yago: a core of semantic knowledge. In Proceedings of the 16th International Conference on World Wide Web, WWW 2007, Banff, Alberta, Canada, May 8-12, 2007, pages 697-706. ACM, 2007. URL: https://doi.org/10.1145/1242572.1242667.
  173. Tianxiang Sun, Yunfan Shao, Xipeng Qiu, Qipeng Guo, Yaru Hu, Xuanjing Huang, and Zheng Zhang. CoLAKE: Contextualized Language and Knowledge Embedding. In Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8-13, 2020, pages 3660-3670. International Committee on Computational Linguistics, 2020. URL: https://doi.org/10.18653/V1/2020.COLING-MAIN.327.
  174. Yu Sun, Shuohuan Wang, Yu-Kun Li, Shikun Feng, Hao Tian, Hua Wu, and Haifeng Wang. ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pages 8968-8975. AAAI Press, 2020. URL: https://doi.org/10.1609/AAAI.V34I05.6428.
  175. Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net, 2019. URL: https://openreview.net/forum?id=HkgEQnRqYQ.
  176. Vinitra Swamy, Angelika Romanou, and Martin Jaggi. Interpreting Language Models Through Knowledge Graph Extraction, 2021. URL: https://doi.org/10.48550/arXiv.2111.08546.
  177. Niket Tandon, Gerard de Melo, and Gerhard Weikum. Acquiring Comparative Commonsense Knowledge from the Web. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014, Québec City, Québec, Canada, pages 166-172. AAAI Press, 2014. URL: https://doi.org/10.1609/AAAI.V28I1.8735.
  178. Hao Tian, Can Gao, Xinyan Xiao, Hao Liu, Bolei He, Hua Wu, Haifeng Wang, and Feng Wu. SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 4067-4076. Association for Computational Linguistics, 2020. URL: https://doi.org/10.18653/V1/2020.ACL-MAIN.374.
  179. Ilaria Tiddi, Mathieu d'Aquin, and Enrico Motta. Dedalo: Looking for Clusters Explanations in a Labyrinth of Linked Data. In The Semantic Web: Trends and Challenges - 11th International Conference, ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014. Proceedings, volume 8465 of Lecture Notes in Computer Science, pages 333-348. Springer, 2014. URL: https://doi.org/10.1007/978-3-319-07443-6_23.
  180. Ilaria Tiddi and Stefan Schlobach. Knowledge graphs as tools for explainable machine learning: A survey. Artificial Intelligence, 302:103627, 2022. URL: https://doi.org/10.1016/J.ARTINT.2021.103627.
  181. Kristina Toutanova and Danqi Chen. Observed versus latent features for knowledge base and text inference. In Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality, CVSC 2015, Beijing, China, July 26-31, 2015, pages 57-66. Association for Computational Linguistics, 2015. URL: https://doi.org/10.18653/V1/W15-4007.
  182. An C. Tran, Jens Dietrich, Hans W. Guesgen, and Stephen Marsland. An Approach to Parallel Class Expression Learning. In Rules on the Web: Research and Applications - 6th International Symposium, RuleML 2012, Montpellier, France, August 27-29, 2012. Proceedings, volume 7438 of Lecture Notes in Computer Science, pages 302-316. Springer, 2012. URL: https://doi.org/10.1007/978-3-642-32689-9_25.
  183. An C. Tran, Jens Dietrich, Hans W. Guesgen, and Stephen Marsland. Parallel Symmetric Class Expression Learning. Journal of Machine Learning Research, 18:64:1-64:34, 2017. URL: http://jmlr.org/papers/v18/14-317.html.
  184. Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. Complex Embeddings for Simple Link Prediction. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, volume 48 of JMLR Workshop and Conference Proceedings, pages 2071-2080. JMLR.org, 2016. URL: http://proceedings.mlr.press/v48/trouillon16.html.
  185. Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha P. Talukdar. Composition-based Multi-Relational Graph Convolutional Networks. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, 2020. URL: https://openreview.net/forum?id=BylA_C4tPr.
  186. Johanna Völker, Daniel Fleischhacker, and Heiner Stuckenschmidt. Automatic acquisition of class disjointness. Journal of Web Semantics, 35:124-139, 2015. URL: https://doi.org/10.1016/J.WEBSEM.2015.07.001.
  187. Denny Vrandecic and Markus Krötzsch. Wikidata: a free collaborative knowledgebase. Communications of the ACM, 57(10):78-85, 2014. URL: https://doi.org/10.1145/2629489.
  188. Jindong Wang, Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, Binxing Jiao, Yue Zhang, and Xing Xie. On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective, 2023. URL: https://doi.org/10.48550/ARXIV.2302.12095.
  189. Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, and Ee-Peng Lim. Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023, pages 2609-2634. Association for Computational Linguistics, 2023. URL: https://doi.org/10.18653/V1/2023.ACL-LONG.147.
  190. Meihong Wang, Linling Qiu, and Xiaoli Wang. A Survey on Knowledge Graph Embeddings for Link Prediction. Symmetry, 13(3):485, 2021. URL: https://doi.org/10.3390/SYM13030485.
  191. Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, Ziyue Huang, Qipeng Guo, Hao Zhang, Haibin Lin, Junbo Zhao, Jinyang Li, Alexander J. Smola, and Zheng Zhang. Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs, 2019. URL: https://doi.org/10.48550/arXiv.1909.01315.
  192. Quan Wang, Zhendong Mao, Bin Wang, and Li Guo. Knowledge Graph Embedding: A Survey of Approaches and Applications. IEEE Transactions on Knowledge and Data Engineering, 29(12):2724-2743, 2017. URL: https://doi.org/10.1109/TKDE.2017.2754499.
  193. Taowei David Wang, Bijan Parsia, and James A. Hendler. A Survey of the Web Ontology Landscape. In The Semantic Web - ISWC 2006, 5th International Semantic Web Conference, ISWC 2006, Athens, GA, USA, November 5-9, 2006, Proceedings, volume 4273 of Lecture Notes in Computer Science, pages 682-694. Springer, 2006. URL: https://doi.org/10.1007/11926078_49.
  194. Wei Wang, Vincent W. Zheng, Han Yu, and Chunyan Miao. A Survey of Zero-Shot Learning: Settings, Methods, and Applications. ACM Transactions on Intelligent Systems and Technology, 10(2):13:1-13:37, 2019. URL: https://doi.org/10.1145/3293318.
  195. Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. KGAT: Knowledge Graph Attention Network for Recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019, pages 950-958. ACM, 2019. URL: https://doi.org/10.1145/3292500.3330989.
  196. Yaqing Wang, Quanming Yao, James T. Kwok, and Lionel M. Ni. Generalizing from a Few Examples: A Survey on Few-shot Learning. ACM Computing Surveys, 53(3):63:1-63:34, 2021. URL: https://doi.org/10.1145/3386252.
  197. Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. Knowledge Graph Embedding by Translating on Hyperplanes. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014, Québec City, Québec, Canada, pages 1112-1119. AAAI Press, 2014. URL: https://doi.org/10.1609/AAAI.V28I1.8870.
  198. Zhichun Wang, Qingsong Lv, Xiaohan Lan, and Yu Zhang. Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, pages 349-357. Association for Computational Linguistics, 2018. URL: https://doi.org/10.18653/V1/D18-1032.
  199. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022. URL: http://papers.nips.cc/paper_files/paper/2022/hash/9d5609613524ecf4f15af0f7b31abca4-Abstract-Conference.html.
  200. Jialin Wu and Raymond J. Mooney. Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, pages 8061-8072. Association for Computational Linguistics, 2022. URL: https://doi.org/10.18653/V1/2022.EMNLP-MAIN.551.
  201. Lingfei Wu, Peng Cui, Jian Pei, and Liang Zhao, editors. Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, 2022. URL: https://doi.org/10.1007/978-981-16-6054-2.
  202. Xiayu Xiang, Zhongru Wang, Yan Jia, and Binxing Fang. Knowledge Graph-Based Clinical Decision Support System Reasoning: A Survey. In Fourth IEEE International Conference on Data Science in Cyberspace, DSC 2019, Hangzhou, China, June 23-25, 2019, pages 373-380. IEEE, 2019. URL: https://doi.org/10.1109/DSC.2019.00063.
  203. Han Xiao, Minlie Huang, and Xiaoyan Zhu. TransG : A Generative Model for Knowledge Graph Embedding. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers. The Association for Computer Linguistics, 2016. URL: https://doi.org/10.18653/V1/P16-1219.
  204. Ning Xie, Farley Lai, Derek Doran, and Asim Kadav. Visual Entailment Task for Visually-Grounded Language Learning, 2018. URL: https://doi.org/10.48550/arXiv.1811.10582.
  205. Chenyan Xiong, Russell Power, and Jamie Callan. Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding. In Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Perth, Australia, April 3-7, 2017, pages 1271-1279. ACM, 2017. URL: https://doi.org/10.1145/3038912.3052558.
  206. Canran Xu and Ruijiang Li. Relation Embedding with Dihedral Group in Knowledge Graph. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pages 263-272. Association for Computational Linguistics, 2019. URL: https://doi.org/10.18653/V1/P19-1026.
  207. Chengjin Xu, Fenglong Su, and Jens Lehmann. Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphs, 2022. URL: https://doi.org/10.48550/ARXIV.2203.02150.
  208. Da Xu, Chuanwei Ruan, Evren Körpeoglu, Sushant Kumar, and Kannan Achan. Product Knowledge Graph Embedding for E-commerce. In WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining, Houston, TX, USA, February 3-7, 2020, pages 672-680. ACM, 2020. URL: https://doi.org/10.1145/3336191.3371778.
  209. Bishan Yang and Tom M. Mitchell. Leveraging Knowledge Bases in LSTMs for Improving Machine Reading. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers, pages 1436-1446. Association for Computational Linguistics, 2017. URL: https://doi.org/10.18653/V1/P17-1132.
  210. Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. Embedding Entities and Relations for Learning and Inference in Knowledge Bases. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015. URL: http://arxiv.org/abs/1412.6575.
  211. Zhen Yao, Wen Zhang, Mingyang Chen, Yufeng Huang, Yi Yang, and Huajun Chen. Analogical Inference Enhanced Knowledge Graph Embedding. In Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February 7-14, 2023, pages 4801-4808. AAAI Press, 2023. URL: https://doi.org/10.1609/AAAI.V37I4.25605.
  212. Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, and Jure Leskovec. QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, pages 535-546. Association for Computational Linguistics, 2021. URL: https://doi.org/10.18653/V1/2021.NAACL-MAIN.45.
  213. Zi Ye, Yogan Jaya Kumar, Goh Ong Sing, Fengyan Song, and Junsong Wang. A Comprehensive Survey of Graph Neural Networks for Knowledge Graphs. IEEE Access, 10:75729-75741, 2022. URL: https://doi.org/10.1109/ACCESS.2022.3191784.
  214. Jason Youn and Ilias Tagkopoulos. KGLM: Integrating Knowledge Graph Structure in Language Models for Link Prediction. In Proceedings of the The 12th Joint Conference on Lexical and Computational Semantics, *SEM@ACL 2023, Toronto, Canada, July 13-14, 2023, pages 217-224. Association for Computational Linguistics, 2023. URL: https://doi.org/10.18653/V1/2023.STARSEM-1.20.
  215. Donghan Yu, Chenguang Zhu, Yiming Yang, and Michael Zeng. JAKET: Joint Pre-training of Knowledge Graph and Language Understanding. In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, pages 11630-11638. AAAI Press, 2022. URL: https://doi.org/10.1609/AAAI.V36I10.21417.
  216. Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, and Meng Jiang. A Survey of Knowledge-enhanced Text Generation. ACM Computing Surveys, 54(11s):227:1-227:38, 2022. URL: https://doi.org/10.1145/3512467.
  217. Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi. From Recognition to Cognition: Visual Commonsense Reasoning. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pages 6720-6731. Computer Vision Foundation / IEEE, 2019. URL: https://doi.org/10.1109/CVPR.2019.00688.
  218. Kunli Zhang, Linkun Cai, Yu Song, Tao Liu, and Yueshu Zhao. Combining external medical knowledge for improving obstetric intelligent diagnosis: model development and validation. JMIR medical informatics, 9(5):e25304, 2021. URL: https://doi.org/10.2196/25304.
  219. Tong Zhang, Cheng Wang, Ning Hu, Minlie Qiu, Chen Tang, Xiaodong He, and Jian Huang. DKPLM: Decomposable Knowledge-Enhanced Pretrained Language Model for Natural Language Understanding. In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelfth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, pages 11 703-11 711. AAAI Press, February 22 - March 1 2022. URL: https://doi.org/10.1609/aaai.v36i10.21425.
  220. Yingying Zhang, Shengsheng Qian, Quan Fang, and Changsheng Xu. Multi-modal Knowledge-aware Hierarchical Attention Network for Explainable Medical Question Answering. In Proceedings of the 27th ACM International Conference on Multimedia, MM 2019, Nice, France, October 21-25, 2019, pages 1089-1097. ACM, 2019. URL: https://doi.org/10.1145/3343031.3351033.
  221. Yang Zhao, Lu Xiang, Junnan Zhu, Jiajun Zhang, Yu Zhou, and Chengqing Zong. Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity. In Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8-13, 2020, pages 4495-4505. International Committee on Computational Linguistics, 2020. URL: https://doi.org/10.18653/V1/2020.COLING-MAIN.397.
  222. Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, and Ed H. Chi. Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. URL: https://openreview.net/pdf?id=WZH7099tgfM.
  223. Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. Graph neural networks: A review of methods and applications. AI Open, 1:57-81, 2020. URL: https://doi.org/10.1016/J.AIOPEN.2021.01.001.
  224. Xiaohan Zhou, Yunhui Yi, and Geng Jia. Path-RotatE: Knowledge Graph Embedding by Relational Rotation of Path in Complex Space. In 10th IEEE/CIC International Conference on Communications in China, ICCC 2021, Xiamen, China, July 28-30, 2021, pages 905-910. IEEE, 2021. URL: https://doi.org/10.1109/ICCC52777.2021.9580273.
  225. Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. Large Language Models are Human-Level Prompt Engineers. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. URL: https://openreview.net/pdf?id=92gvk82DE-.
  226. Yu Zhou, Haixia Zheng, Xin Huang, Shufeng Hao, Dengao Li, and Jumin Zhao. Graph Neural Networks: Taxonomy, Advances, and Trends. ACM Transactions on Intelligent Systems and Technology, 13(1):15:1-15:54, 2022. URL: https://doi.org/10.1145/3495161.
  227. Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal A. C. Xhonneux, and Jian Tang. Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 29476-29490, 2021. URL: https://proceedings.neurips.cc/paper/2021/hash/f6a673f09493afcd8b129a0bcf1cd5bc-Abstract.html.
  228. Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He. A Comprehensive Survey on Transfer Learning. Proceedings of the IEEE, 109(1):43-76, 2021. URL: https://doi.org/10.1109/JPROC.2020.3004555.
  229. Terry Yue Zhuo, Yujin Huang, Chunyang Chen, and Zhenchang Xing. Exploring AI Ethics of ChatGPT: A Diagnostic Analysis, 2023. URL: https://doi.org/10.48550/ARXIV.2301.12867.
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