Knowledge Graph Embeddings: Open Challenges and Opportunities

Authors Russa Biswas , Lucie-Aimée Kaffee , Michael Cochez , Stefania Dumbrava , Theis E. Jendal , Matteo Lissandrini , Vanessa Lopez , Eneldo Loza Mencía , Heiko Paulheim , Harald Sack , Edlira Kalemi Vakaj , Gerard de Melo



PDF
Thumbnail PDF

File

TGDK.1.1.4.pdf
  • Filesize: 1.48 MB
  • 32 pages

Document Identifiers

Author Details

Russa Biswas
  • Hasso-Plattner Institut, Potsdam, Germany
Lucie-Aimée Kaffee
  • Hasso-Plattner-Institut, Potsdam, Germany
Michael Cochez
  • Vrije Universiteit Amsterdam, The Netherlands
  • Elsevier Discovery Lab, Netherlands
Stefania Dumbrava
  • ENSIIE, France
Theis E. Jendal
  • Aalborg University, Denmark
Matteo Lissandrini
  • Aalborg University, Denmark
Vanessa Lopez
  • IBM Research Dublin, Ireland
Eneldo Loza Mencía
  • research.eneldo.net, Frankfurt, Germany
Heiko Paulheim
  • Universität Mannheim, Germany
Harald Sack
  • FIZ Karlsruhe, Germany
  • Karlsruhe Institute of Technology, AIFB, Germany
Edlira Kalemi Vakaj
  • Birmingham City University, UK
Gerard de Melo
  • Hasso-Plattner Institut, Potsdam, Germany
  • University of Potsdam, Germany

Cite AsGet BibTex

Russa Biswas, Lucie-Aimée Kaffee, Michael Cochez, Stefania Dumbrava, Theis E. Jendal, Matteo Lissandrini, Vanessa Lopez, Eneldo Loza Mencía, Heiko Paulheim, Harald Sack, Edlira Kalemi Vakaj, and Gerard de Melo. Knowledge Graph Embeddings: Open Challenges and Opportunities. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 4:1-4:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/TGDK.1.1.4

Abstract

While Knowledge Graphs (KGs) have long been used as valuable sources of structured knowledge, in recent years, KG embeddings have become a popular way of deriving numeric vector representations from them, for instance, to support knowledge graph completion and similarity search. This study surveys advances as well as open challenges and opportunities in this area. For instance, the most prominent embedding models focus primarily on structural information. However, there has been notable progress in incorporating further aspects, such as semantics, multi-modal, temporal, and multilingual features. Most embedding techniques are assessed using human-curated benchmark datasets for the task of link prediction, neglecting other important real-world KG applications. Many approaches assume a static knowledge graph and are unable to account for dynamic changes. Additionally, KG embeddings may encode data biases and lack interpretability. Overall, this study provides an overview of promising research avenues to learn improved KG embeddings that can address a more diverse range of use cases.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Machine learning approaches
  • Computing methodologies → Semantic networks
Keywords
  • Knowledge Graphs
  • KG embeddings
  • Link prediction
  • KG applications

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Farahnaz Akrami, Mohammed Samiul Saeef, Qingheng Zhang, Wei Hu, and Chengkai Li. Realistic re-evaluation of knowledge graph completion methods: An experimental study. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pages 1995-2010. ACM, 2020. URL: https://doi.org/10.1145/3318464.3380599.
  2. Mehwish Alam, George Fletcher, Antonie Isaac, Aidan Hogan, Diana Maynard, Heiko Paulheim, Harald Sack, Elena Simperl, Lise Stork, Marieke van Erp, and Hideaki Takeda. Bias in knowledge graph systems. In Proc. of Dagstuhl Seminar 22372: Knowledge Graphs and their Role in the Knowledge Engineering of the 21st Century, 2022; Vol. 12(9), pages 106-109. Dagstuhl Reports, 2022. URL: https://doi.org/10.4230/DagRep.12.9.60.
  3. Mirza Mohtashim Alam, Md Rashad Al Hasan Rony, Mojtaba Nayyeri, Karishma Mohiuddin, MST Mahfuja Akter, Sahar Vahdati, and Jens Lehmann. Language model guided knowledge graph embeddings. IEEE Access, 10:76008-76020, 2022. URL: https://doi.org/10.1109/ACCESS.2022.3191666.
  4. Dimitrios Alivanistos, Max Berrendorf, Michael Cochez, and Mikhail Galkin. Query embedding on hyper-relational knowledge graphs. In 10th International Conference on Learning Representations, ICLR 2020, virtual, April 25-29, 2022. OpenReview.net, 2022. URL: https://openreview.net/forum?id=4rLw09TgRw9.
  5. Mona Alshahrani, Maha A Thafar, and Magbubah Essack. Application and evaluation of knowledge graph embeddings in biomedical data. PeerJ Computer Science, 7:e341, feb 2021. URL: https://doi.org/10.7717/PEERJ-CS.341.
  6. Faisal Alshargi, Saeedeh Shekarpour, Tommaso Soru, and Amit Sheth. Concept2vec: Metrics for evaluating quality of embeddings for ontological concepts. arXiv preprint arXiv:1803.04488, abs/1803.04488, 2018. URL: https://doi.org/10.48550/arXiv.1803.04488.
  7. Erik Arakelyan, Daniel Daza, Pasquale Minervini, and Michael Cochez. Complex query answering with neural link predictors. In International Conference on Learning Representations. OpenReview.net, 2021. URL: https://openreview.net/forum?id=Mos9F9kDwkz.
  8. Erik Arakelyan, Pasquale Minervini, Daniel Daza, Michael Cochez, and Isabelle Augenstein. Adapting neural link predictors for data-efficient complex query answering. arXiv, jan 2023. URL: https://arxiv.org/abs/2301.12313.
  9. Mario Arduini, Lorenzo Noci, Federico Pirovano, Ce Zhang, Yash Raj Shrestha, and Bibek Paudel. Adversarial learning for debiasing knowledge graph embeddings. ArXiv, abs/2006.16309, jun 2020. URL: https://doi.org/10.48550/arXiv.2006.16309.
  10. Siddhant Arora, Srikanta Bedathur, Maya Ramanath, and Deepak Sharma. IterefinE: Iterative KG refinement embeddings using symbolic knowledge. In Automated Knowledge Base Construction, 2020. URL: https://doi.org/10.24432/C5NP46.
  11. Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. DBpedia: A nucleus for a web of open data. In The semantic web, volume 4825, pages 722-735. Springer Berlin Heidelberg, 2007. URL: https://doi.org/10.1007/978-3-540-76298-0_52.
  12. Luyi Bai, Xiangnan Ma, Mingcheng Zhang, and Wenting Yu. TPmod: A tendency-guided prediction model for temporal knowledge graph completion. ACM Transactions on Knowledge Discovery from Data, 15(3):1-17, jun 2021. URL: https://doi.org/10.1145/3443687.
  13. Yushi Bai, Xin Lv, Juanzi Li, and Lei Hou. Answering complex logical queries on knowledge graphs via query computation tree optimization. arXiv preprint arXiv:2212.09567, abs/2212.09567, dec 2022. URL: https://doi.org/10.48550/ARXIV.2212.09567.
  14. Ivana Balažević, Carl Allen, and Timothy M Hospedales. Hypernetwork knowledge graph embeddings. In Proceedings of the International Conference on Artificial Neural Networks, volume 11731, pages 553-565. Springer International Publishing, 2019. URL: https://doi.org/10.1007/978-3-030-30493-5_52.
  15. Ivana Balažević, Carl Allen, and Timothy M. Hospedales. TuckER: Tensor factorization for knowledge graph completion. 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 5184-5193. Association for Computational Linguistics, 2019. URL: https://doi.org/10.18653/V1/D19-1522.
  16. Matthias Baumgartner, Daniele Dell’Aglio, Heiko Paulheim, and Abraham Bernstein. Towards the web of embeddings: Integrating multiple knowledge graph embedding spaces with FedCoder. Journal of Web Semantics, 75:100741, 2023. URL: https://doi.org/10.1016/J.WEBSEM.2022.100741.
  17. Russa Biswas, Harald Sack, and Mehwish Alam. MADLINK: Attentive multihop and entity descriptions for link prediction in knowledge graphs. Semantic Web, pages 1-24, 2022. URL: https://doi.org/10.3233/sw-222960.
  18. Russa Biswas, Radina Sofronova, Mehwish Alam, and Harald Sack. Contextual language models for knowledge graph completion. In MLSMKG, volume 2997. CEUR-WS.org, 2021. URL: https://ceur-ws.org/Vol-2997/paper3.pdf.
  19. 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 European Semantic Web Conference, volume 12731, pages 614-630. Springer International Publishing, 2021. URL: https://doi.org/10.1007/978-3-030-77385-4_37.
  20. 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, pages 1962-1963. AAAI Press, jul 2007. URL: http://www.aaai.org/Library/AAAI/2007/aaai07-355.php.
  21. Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems, 26:2787-2795, 2013. URL: https://proceedings.neurips.cc/paper/2013/hash/1cecc7a77928ca8133fa24680a88d2f9-Abstract.html.
  22. Antoine Bordes, Jason Weston, Ronan Collobert, and Yoshua Bengio. Learning structured embeddings of knowledge bases. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, volume 25, pages 301-306. Association for the Advancement of Artificial Intelligence (AAAI), 2011. URL: https://doi.org/10.1609/AAAI.V25I1.7917.
  23. Borui Cai, Yong Xiang, Longxiang Gao, He Zhang, Yunfeng Li, and Jianxin Li. Temporal knowledge graph completion: A survey. arXiv preprint arXiv:2201.08236, abs/2201.08236:6545-6553, jan 2022. URL: https://doi.org/10.24963/IJCAI.2023/734.
  24. Alison Callahan, Jose Cruz-Toledo, Peter Ansell, and Michel Dumontier. Bio2RDF release 2: improved coverage, interoperability and provenance of life science linked data. In Extended semantic web conference, volume 7882, pages 200-212. Springer, Springer Berlin Heidelberg, 2013. URL: https://doi.org/10.1007/978-3-642-38288-8_14.
  25. Soumen Chakrabarti, Harkanwar Singh, Shubham Lohiya, Prachi Jain, and Mausam. Joint completion and alignment of multilingual knowledge graphs. 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 11922-11938. Association for Computational Linguistics, 2022. URL: https://doi.org/10.18653/V1/2022.EMNLP-MAIN.817.
  26. Kai-Wei Chang, Wen-tau Yih, Bishan Yang, and Christopher Meek. Typed tensor decomposition of knowledge bases for relation extraction. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1568-1579. Association for Computational Linguistics, 2014. URL: https://doi.org/10.3115/V1/D14-1165.
  27. Xuelu Chen, Muhao Chen, Changjun Fan, Ankith Uppunda, Yizhou Sun, and Carlo Zaniolo. Multilingual knowledge graph completion via ensemble knowledge transfer. In Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16-20 November 2020, volume EMNLP 2020 of Findings of ACL, pages 3227-3238. Association for Computational Linguistics, 2020. URL: https://doi.org/10.18653/V1/2020.FINDINGS-EMNLP.290.
  28. Yihong Chen, Pasquale Minervini, Sebastian Riedel, and Pontus Stenetorp. Relation prediction as an auxiliary training objective for improving multi-relational graph representations. In 3rd Conference on Automated Knowledge Base Construction, AKBC 2021, Virtual, October 4-8, 2021, 2021. URL: https://doi.org/10.24432/C54K5W.
  29. Bonggeun Choi, Daesik Jang, and Youngjoong Ko. MEM-KGC: Masked entity model for knowledge graph completion with pre-trained language model. IEEE Access, 9:132025-132032, 2021. URL: https://doi.org/10.1109/ACCESS.2021.3113329.
  30. Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan, Max Berrendorf, Daniel Daza, Mikhail Galkin, Pasquale Minervini, Mathias Niepert, and Hongyu Ren. Approximate answering of graph queries. In Compendium of Neurosymbolic Artificial Intelligence, volume 369, pages 373-386. IOS Press, aug 2023. URL: https://doi.org/10.3233/FAIA230149.
  31. Michael Cochez, Martina Garofalo, Jérôme Lenßen, and Maria Angela Pellegrino. A first experiment on including text literals in KGloVe. In Joint proceedings of the 4th Workshop on Semantic Deep Learning (SemDeep-4) and NLIWoD4: Natural Language Interfaces for the Web of Data (NLIWOD-4) and 9th Question Answering over Linked Data challenge (QALD-9) co-located with 17th International Semantic Web Conference (ISWC 2018), Monterey, California, United States of America, October 8th - 9th, 2018, volume 2241 of CEUR Workshop Proceedings, pages 103-106. CEUR-WS.org, 2018. URL: https://ceur-ws.org/Vol-2241/paper-10.pdf.
  32. Tu Dinh Nguyen Dai Quoc Nguyen, Dat Quoc Nguyen, and Dinh Phung. A novel embedding model for knowledge base completion based on convolutional neural network. In Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 327-333. Association for Computational Linguistics, 2018. URL: https://doi.org/10.18653/V1/N18-2053.
  33. Shib Sankar Dasgupta, Swayambhu Nath Ray, and Partha Talukdar. HyTE: Hyperplane-based temporally aware knowledge graph embedding. In Proceedings of the 2018 conference on empirical methods in natural language processing, pages 2001-2011. Association for Computational Linguistics, 2018. URL: https://doi.org/10.18653/V1/D18-1225.
  34. Daniel Daza, Dimitrios Alivanistos, Payal Mitra, Thom Pijnenburg, Michael Cochez, and Paul Groth. BioBLP: A modular framework for learning on multimodal biomedical knowledge graphs. arXiv preprint arXiv:2306.03606, 2023. URL: https://doi.org/10.48550/ARXIV.2306.03606.
  35. Daniel Daza and Michael Cochez. Message passing query embedding. In ICML Workshop - Graph Representation Learning and Beyond, 2020. URL: https://doi.org/10.48550/arXiv.2002.02406.
  36. Daniel Daza, Michael Cochez, and Paul Groth. Inductive entity representations from text via link prediction. In Proceedings of the Web Conference 2021, pages 798-808. ACM, 2021. URL: https://doi.org/10.1145/3442381.3450141.
  37. Gianluca Demartini. Implicit bias in crowdsourced knowledge graphs. In Companion Proceedings of The 2019 World Wide Web Conference, pages 624-630. Association for Computing Machinery, 2019. URL: https://doi.org/10.1145/3308560.3317307.
  38. Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. Convolutional 2D knowledge graph embeddings. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, pages 1811-1818. Association for the Advancement of Artificial Intelligence (AAAI), 2018. URL: https://doi.org/10.1609/AAAI.V32I1.11573.
  39. Boyang Ding, Quan Wang, Bin Wang, and Li Guo. Improving knowledge graph embedding using simple constraints. arXiv preprint arXiv:1805.02408, pages 110-121, 2018. URL: https://doi.org/10.18653/V1/P18-1011.
  40. Claudia d’Amato, Nicola Flavio Quatraro, and Nicola Fanizzi. Injecting background knowledge into embedding models for predictive tasks on knowledge graphs. In European Semantic Web Conference, volume 12731, pages 441-457. Springer, Springer International Publishing, 2021. URL: https://doi.org/10.1007/978-3-030-77385-4_26.
  41. Yasha Ektefaie, George Dasoulas, Ayusa Noori, Maha Farhat, and Marinka Zitnik. Multimodal learning with graphs. Nature Machine Intelligence, 5:340-350, 2023. URL: https://doi.org/10.1038/S42256-023-00624-6.
  42. Michael Ellers, Michael Cochez, Tobias Schumacher, Markus Strohmaier, and Florian Lemmerich. Privacy attacks on network embeddings. CoRR, abs/1912.10979, 2019. URL: https://doi.org/10.48550/arXiv.1912.10979.
  43. Shahla Farzana, Qunzhi Zhou, and Petar Ristoski. Knowledge graph-enhanced neural query rewriting. In Companion Proceedings of the ACM Web Conference 2023, pages 911-919. ACM, apr 2023. URL: https://doi.org/10.1145/3543873.3587678.
  44. Jun Feng, Minlie Huang, Yang Yang, and Xiaoyan Zhu. GAKE: Graph aware knowledge embedding. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 641-651. ACL, 2016. URL: https://aclanthology.org/C16-1062/.
  45. Joseph Fisher. Measuring social bias in knowledge graph embeddings. ArXiv, abs/1912.02761, dec 2019. URL: https://doi.org/10.48550/arXiv.1912.02761.
  46. Joseph Fisher, Arpit Mittal, Dave Palfrey, and Christos Christodoulopoulos. Debiasing knowledge graph embeddings. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7332-7345. Association for Computational Linguistics, 2020. URL: https://doi.org/10.18653/V1/2020.EMNLP-MAIN.595.
  47. Mikhail Galkin, Max Berrendorf, and Charles Tapley Hoyt. An open challenge for inductive link prediction on knowledge graphs. arXiv preprint arXiv:2203.01520, abs/2203.01520, mar 2022. URL: https://doi.org/10.48550/ARXIV.2203.01520.
  48. Mikhail Galkin, Etienne Denis, Jiapeng Wu, and William L. Hamilton. NodePiece: Compositional and parameter-efficient representations of large knowledge graphs. In International Conference on Learning Representations. OpenReview.net, 2022. URL: https://openreview.net/forum?id=xMJWUKJnFSw.
  49. Min Gao, Jian-Yu Li, Chun-Hua Chen, Yun Li, Jun Zhang, and Zhi-Hui Zhan. Enhanced multi-task learning and knowledge graph-based recommender system. IEEE Transactions on Knowledge and Data Engineering, 35:10281-10294, 2023. URL: https://doi.org/10.1109/TKDE.2023.3251897.
  50. Alberto García-Durán, Sebastijan Dumancic, and Mathias Niepert. Learning sequence encoders for temporal knowledge graph completion. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4816-4821. Association for Computational Linguistics, Association for Computational Linguistics, 2018. URL: https://doi.org/10.18653/v1/d18-1516.
  51. Alberto García-Durán and Mathias Niepert. KBLRN: End-to-end learning of knowledge base representations with latent, relational, and numerical features. In Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, UAI 2018, Monterey, California, USA, August 6-10, 2018, pages 372-381. AUAI Press, 2018. URL: http://auai.org/uai2018/proceedings/papers/149.pdf.
  52. Dinesh Garg, Shajith Ikbal, Santosh K Srivastava, Harit Vishwakarma, Hima Karanam, and L Venkata Subramaniam. Quantum embedding of knowledge for reasoning. Advances in Neural Information Processing Systems, 32:5595-5605, 2019. URL: https://proceedings.neurips.cc/paper/2019/hash/cb12d7f933e7d102c52231bf62b8a678-Abstract.html.
  53. Aryo Pradipta Gema, Dominik Grabarczyk, Wolf De Wulf, Piyush Borole, Javier Antonio Alfaro, Pasquale Minervini, Antonio Vergari, and Ajitha Rajan. Knowledge graph embeddings in the biomedical domain: Are they useful? a look at link prediction, rule learning, and downstream polypharmacy tasks. arXiv.org, abs/2305.19979, may 2023. URL: https://doi.org/10.48550/ARXIV.2305.19979.
  54. Genet Asefa Gesese, Russa Biswas, Mehwish Alam, and Harald Sack. A survey on knowledge graph embeddings with literals: Which model links better literal-ly? Semantic Web, 12:617-647, 2021. URL: https://doi.org/10.3233/SW-200404.
  55. Paul Groth, Elena Simperl, Marieke van Erp, and Denny Vrandečić. Knowledge graphs and their role in the knowledge engineering of the 21st century (Dagstuhl seminar 22372). Dagstuhl Reports, 12:60-120, 2023. URL: https://doi.org/10.4230/DAGREP.12.9.60.
  56. Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pages 855-864. ACM, 2016. URL: https://doi.org/10.1145/2939672.2939754.
  57. 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, pages 192-202. Association for Computational Linguistics, 2016. URL: https://doi.org/10.18653/V1/D16-1019.
  58. Shu Guo, Quan Wang, Lihong Wang, Bin Wang, and Li Guo. Knowledge graph embedding with iterative guidance from soft rules. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, pages 4816-4823. Association for the Advancement of Artificial Intelligence (AAAI), 2018. URL: https://doi.org/10.1609/AAAI.V32I1.11918.
  59. Ferras Hamad, Issac Liu, and Xian Xing Zhang. Food discovery with uber eats: Building a query understanding engine. uber engineering blog. https://eng.uber.com/uber-eats-query-understanding/., 2018. Accessed: 2022-07-03. Google Scholar
  60. William L. Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, and Jure Leskovec. Embedding logical queries on knowledge graphs. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, pages 2030-2041, 2018. URL: https://proceedings.neurips.cc/paper/2018/hash/ef50c335cca9f340bde656363ebd02fd-Abstract.html.
  61. William L Hamilton, Rex Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems, pages 1025-1035, 2017. URL: https://proceedings.neurips.cc/paper/2017/hash/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html.
  62. Xiaolin Han, Daniele Dell’Aglio, Tobias Grubenmann, Reynold Cheng, and Abraham Bernstein. A framework for differentially-private knowledge graph embeddings. Journal of Web Semantics, 72:100696, 2022. URL: https://doi.org/10.1016/J.WEBSEM.2021.100696.
  63. Zhen Han, Peng Chen, Yunpu Ma, and Volker Tresp. Explainable subgraph reasoning for forecasting on temporal knowledge graphs. In International Conference on Learning Representations, 2020. URL: https://openreview.net/forum?id=pGIHq1m7PU.
  64. Zhen Han, Zifeng Ding, Yunpu Ma, Yujia Gu, and Volker Tresp. Learning neural ordinary equations for forecasting future links on temporal knowledge graphs. In Proceedings of the 2021 conference on empirical methods in natural language processing, pages 8352-8364. Association for Computational Linguistics, 2021. URL: https://doi.org/10.18653/V1/2021.EMNLP-MAIN.658.
  65. Junheng Hao, Muhao Chen, Wenchao Yu, Yizhou Sun, and Wei Wang. Universal representation learning of knowledge bases by jointly embedding instances and ontological concepts. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1709-1719. ACM, 2019. URL: https://doi.org/10.1145/3292500.3330838.
  66. Ming He, Xiangkun Du, and Bo Wang. Representation learning of knowledge graphs via fine-grained relation description combinations. IEEE Access, 7:26466-26473, 2019. URL: https://doi.org/10.1109/ACCESS.2019.2901544.
  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 on conference on information and knowledge management, pages 623-632. ACM, 2015. URL: https://doi.org/10.1145/2806416.2806502.
  68. Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. LightGCN: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 639-648. ACM, 2020. URL: https://doi.org/10.1145/3397271.3401063.
  69. Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web, pages 173-182. International World Wide Web Conferences Steering Committee, 2017. URL: https://doi.org/10.1145/3038912.3052569.
  70. Nicolas Heist, Sven Hertling, Daniel Ringler, and Heiko Paulheim. Knowledge graphs on the web-an overview. Knowledge Graphs for eXplainable Artificial Intelligence, pages 3-22, 2020. URL: https://doi.org/10.3233/SSW200009.
  71. Kexin Huang, Payal Chandak, Qianwen Wang, Shreyas Havaldar, Akhil Vaid, Jure Leskovec, Girish Nadkarni, Benjamin Glicksberg, Nils Gehlenborg, and Marinka Zitnik. Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design. medRxiv, 2023. URL: https://doi.org/10.1101/2023.03.19.23287458.
  72. Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik. Artificial intelligence foundation for therapeutic science. Nature chemical biology, 18:1033-1036, 2022. URL: https://doi.org/10.1038/s41589-022-01131-2.
  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, pages 105-113. ACM, 2019. URL: https://doi.org/10.1145/3289600.3290956.
  74. Zijie Huang, Zheng Li, Haoming Jiang, Tianyu Cao, Hanqing Lu, Bing Yin, Karthik Subbian, Yizhou Sun, and Wei Wang. Multilingual knowledge graph completion with self-supervised adaptive graph alignment. 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, volume abs/2203.14987, pages 474-485. Association for Computational Linguistics, 2022. URL: https://doi.org/10.18653/V1/2022.ACL-LONG.36.
  75. Nicolas Hubert, Pierre Monnin, Armelle Brun, and Davy Monticolo. Knowledge graph embeddings for link prediction: Beware of semantics! In DL4KG@ ISWC 2022: Workshop on Deep Learning for Knowledge Graphs, held as part of ISWC 2022: the 21st International Semantic Web Conference, volume 3342. CEUR-WS.org, 2022. URL: https://ceur-ws.org/Vol-3342/paper-4.pdf.
  76. Andreea Iana and Heiko Paulheim. More is not always better: The negative impact of A-box materialization on RDF2vec knowledge graph embeddings. In Workshop on Combining Symbolic and Sub-symbolic Methods and their Applications (CSSA), volume abs/2009.00318. CEUR-WS.org, sep 2020. URL: https://ceur-ws.org/Vol-2699/paper05.pdf.
  77. 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 18, volume 12731, pages 143-159. Springer, Springer International Publishing, 2021. URL: https://doi.org/10.1007/978-3-030-77385-4_9.
  78. Nitisha Jain, Trung-Kien Tran, Mohamed H Gad-Elrab, and Daria Stepanova. Improving knowledge graph embeddings with ontological reasoning. In International Semantic Web Conference, volume 12922, pages 410-426. Springer, Springer International Publishing, 2021. URL: https://doi.org/10.1007/978-3-030-88361-4_24.
  79. Krzysztof Janowicz, Bo Yan 0003, Blake Regalia, Rui Zhu, and Gengchen Mai. Debiasing knowledge graphs: Why female presidents are not like female popes. 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: http://ceur-ws.org/Vol-2180/ISWC_2018_Outrageous_Ideas_paper_17.pdf.
  80. Kanchan Jha, Sriparna Saha, and Snehanshu Saha. Prediction of protein-protein interactions using deep multi-modal representations. In 2021 International Joint Conference on Neural Networks (IJCNN), pages 1-8. IEEE, jul 2021. URL: https://doi.org/10.1109/IJCNN52387.2021.9533478.
  81. Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. Knowledge graph embedding via dynamic mapping matrix. In Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing, pages 687-696. Association for Computational Linguistics, 2015. URL: https://doi.org/10.3115/V1/P15-1067.
  82. Guoliang Ji, Kang Liu, Shizhu He, and Jun Zhao. Knowledge graph completion with adaptive sparse transfer matrix. In Proceedings of the Thirtieth AAAI conference on artificial intelligence, volume 30, pages 985-991. Association for the Advancement of Artificial Intelligence (AAAI), 2016. URL: https://doi.org/10.1609/AAAI.V30I1.10089.
  83. Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and S Yu Philip. A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Transactions on Neural Networks and Learning Systems, 33(2):494-514, 2021. URL: https://doi.org/10.1109/TNNLS.2021.3070843.
  84. Ningning Jia, Xiang Cheng, and Sen Su. Improving knowledge graph embedding using locally and globally attentive relation paths. In European Conference on Information Retrieval, volume 12035, pages 17-32. Springer International Publishing, 2020. URL: https://doi.org/10.1007/978-3-030-45439-5_2.
  85. Yantao Jia, Yuanzhuo Wang, Hailun Lin, Xiaolong Jin, and Xueqi Cheng. Locally adaptive translation for knowledge graph embedding. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, volume 30, pages 992-998. Association for the Advancement of Artificial Intelligence (AAAI), 2016. URL: https://doi.org/10.1609/AAAI.V30I1.10091.
  86. Woojeong Jin, Meng Qu, Xisen Jin, and Xiang Ren. Recurrent event network: Autoregressive structure inferenceover temporal knowledge graphs. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6669-6683. Association for Computational Linguistics, 2020. URL: https://doi.org/10.18653/V1/2020.EMNLP-MAIN.541.
  87. Jaehun Jung, Jinhong Jung, and U Kang. Learning to walk across time for interpretable temporal knowledge graph completion. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 786-795. ACM, 2021. URL: https://doi.org/10.1145/3447548.3467292.
  88. Lucie-Aimée Kaffee. Multilinguality in knowledge graphs. PhD thesis, University of Southampton, 2021. URL: https://eprints.soton.ac.uk/456783/.
  89. Lucie-Aimée Kaffee, Alessandro Piscopo, Pavlos Vougiouklis, Elena Simperl, Leslie Carr, and Lydia Pintscher. A glimpse into babel: An analysis of multilinguality in wikidata. In Proceedings of the 13th International Symposium on Open Collaboration, OpenSym 2017, Galway, Ireland, August 23-25, 2017, pages 14:1-14:5. ACM, 2017. URL: https://doi.org/10.1145/3125433.3125465.
  90. Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, and Pascal Poupart. Representation learning for dynamic graphs: A survey. The Journal of Machine Learning Research, 21:2648-2720, 2020. URL: http://jmlr.org/papers/v21/19-447.html.
  91. Seyed Mehran Kazemi and David Poole. Simple embedding for link prediction in knowledge graphs. Advances in neural information processing systems, 31:4289-4300, 2018. URL: https://proceedings.neurips.cc/paper/2018/hash/b2ab001909a8a6f04b51920306046ce5-Abstract.html.
  92. Bosung Kim, Taesuk Hong, Youngjoong Ko, and Jungyun Seo. Multi-task learning for knowledge graph completion with pre-trained language models. In COLING, pages 1737-1743. International Committee on Computational Linguistics, 2020. URL: https://doi.org/10.18653/V1/2020.COLING-MAIN.153.
  93. Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, abs/1609.02907, 2016. URL: https://doi.org/10.48550/arXiv.1609.02907.
  94. Franz Krause. Dynamic knowledge graph embeddings via local embedding reconstructions. In The Semantic Web: ESWC 2022 Satellite Events: Hersonissos, Crete, Greece, May 29-June 2, 2022, Proceedings, volume 13384, pages 215-223. Springer, 2022. URL: https://doi.org/10.1007/978-3-031-11609-4_36.
  95. Franz Krause, Tobias Weller, and Heiko Paulheim. On a generalized framework for time-aware knowledge graphs. In Towards a Knowledge-Aware AI: SEMANTiCS 2022—Proceedings of the 18th International Conference on Semantic Systems, 13-15 September 2022, Vienna, Austria, volume 55, page 69. IOS Press, IOS Press, 2022. URL: https://doi.org/10.3233/ssw220010.
  96. Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al. Visual genome: Connecting language and vision using crowdsourced dense image annotations. International Journal of Computer Vision, 123:32-73, 2017. URL: https://doi.org/10.1007/S11263-016-0981-7.
  97. Arun Krishnan. Making search easier: How amazon’s product graph is helping customers find products more easily. amazon blog. https://blog.aboutamazon.com/innovation/making-search-easier, 2018. Accessed: 2022-07-03.
  98. Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, and Asja Fischer. Incorporating literals into knowledge graph embeddings. In Proceedings of the International Semantic Web Conference, volume 11778, pages 347-363. Springer International Publishing, 2019. URL: https://doi.org/10.1007/978-3-030-30793-6_20.
  99. Denis Krompaß, Stephan Baier, and Volker Tresp. Type-constrained representation learning in knowledge graphs. In International semantic web conference, volume 9366, pages 640-655. Springer, Springer International Publishing, 2015. URL: https://doi.org/10.1007/978-3-319-25007-6_37.
  100. Timothée Lacroix, Guillaume Obozinski, and Nicolas Usunier. Tensor decompositions for temporal knowledge base completion. In International Conference on Learning Representations, 2019. URL: https://openreview.net/forum?id=rke2P1BFwS.
  101. Hoang Thanh Lam, Marco Luca Sbodio, Marcos Martinez Gallindo, Mykhaylo Zayats, Raul Fernandez-Diaz, Victor Valls, Gabriele Picco, Cesar Berrospi Ramis, and Vanessa Lopez. Otter-knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discovery. arXiv.org, abs/2306.12802, jun 2023. URL: https://doi.org/10.48550/ARXIV.2306.12802.
  102. Julien Leblay and Melisachew Wudage Chekol. Deriving validity time in knowledge graph. In Companion Proceedings of the The Web Conference 2018, pages 1771-1776. ACM Press, 2018. URL: https://doi.org/10.1145/3184558.3191639.
  103. Julien Leblay, Melisachew Wudage Chekol, and Xin Liu. Towards temporal knowledge graph embeddings with arbitrary time precision. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pages 685-694. ACM, 2020. URL: https://doi.org/10.1145/3340531.3412028.
  104. Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, et al. Dbpedia-a large-scale, multilingual knowledge base extracted from Wikipedia. Semantic web, 6(2):167-195, 2015. URL: https://doi.org/10.3233/SW-140134.
  105. Da Li, Ming Yi, and Yukai He. LP-BERT: Multi-task pre-training knowledge graph bert for link prediction. arXiv preprint arXiv:2201.04843, abs/2201.04843, 2022. URL: https://doi.org/10.48550/arXiv.2201.04843.
  106. Michelle M Li, Kexin Huang, and Marinka Zitnik. Graph representation learning in biomedicine and healthcare. Nature Biomedical Engineering, 6:1-17, 2022. URL: https://doi.org/10.1038/s41551-022-00942-x.
  107. Qian Li, Daling Wang, Shi Feng, Cheng Niu, and Yifei Zhang. Global graph attention embedding network for relation prediction in knowledge graphs. IEEE Transactions on Neural Networks and Learning Systems, 33:6712-6725, 2021. URL: https://doi.org/10.1109/TNNLS.2021.3083259.
  108. Yuezhang Li, Ronghuo Zheng, Tian Tian, Zhiting Hu, Rahul Iyer, and Katia P. Sycara. Joint embedding of hierarchical categories and entities for concept categorization and dataless classification. In COLING, volume abs/1607.07956, pages 2678-2688. ACL, 2016. URL: https://aclanthology.org/C16-1252/.
  109. Zixuan Li, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, Huawei Shen, Yuanzhuo Wang, and Xueqi Cheng. Temporal knowledge graph reasoning based on evolutional representation learning. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pages 408-417. ACM, 2021. URL: https://doi.org/10.1145/3404835.3462963.
  110. Paul Pu Liang, Yiwei Lyu, Xiang Fan, Zetian Wu, Yun Cheng, Jason Wu, Leslie Chen, Peter Wu, Michelle A. Lee, Yuke Zhu, Ruslan Salakhutdinov, and Louis-Philippe Morency. MultiBench: Multiscale benchmarks for multimodal representation learning. CoRR, abs/2107.07502, 2021. URL: https://doi.org/10.48550/arXiv.2107.07502.
  111. Siyuan Liao, Shangsong Liang, Zaiqiao Meng, and Qiang Zhang. Learning dynamic embeddings for temporal knowledge graphs. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pages 535-543. ACM, 2021. URL: https://doi.org/10.1145/3437963.3441741.
  112. Lifan Lin and Kun She. Tensor decomposition-based temporal knowledge graph embedding. In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), pages 969-975. IEEE, IEEE, nov 2020. URL: https://doi.org/10.1109/ICTAI50040.2020.00151.
  113. Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, and Song Liu. Modeling relation paths for representation learning of knowledge bases. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, volume abs/1506.00379, pages 705-714. Association for Computational Linguistics, 2015. URL: https://doi.org/10.18653/V1/D15-1082.
  114. Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. Learning entity and relation embeddings for knowledge graph completion. In Proceedings of the AAAI conference on artificial intelligence, volume 29, pages 2181-2187. Association for the Advancement of Artificial Intelligence (AAAI), 2015. URL: https://doi.org/10.1609/AAAI.V29I1.9491.
  115. Sedigheh Mahdavi, Shima Khoshraftar, and Aijun An. dynnode2vec: Scalable dynamic network embedding. In 2018 IEEE International Conference on Big Data (Big Data), pages 3762-3765. IEEE, IEEE, 2018. URL: https://doi.org/10.1109/BIGDATA.2018.8621910.
  116. Pasquale Minervini, Thomas Demeester, Tim Rocktäschel, and Sebastian Riedel. Adversarial sets for regularising neural link predictors. In UAI2017, the 33rd Conference on Uncertainty in Artificial Intelligence, volume abs/1707.07596, pages 1-10. AUAI Press, 2017. URL: http://auai.org/uai2017/proceedings/papers/306.pdf.
  117. Seungwhan Moon, Pararth Shah, Anuj Kumar, and Rajen Subba. OpenDialKG: Explainable conversational reasoning with attention-based walks over knowledge graphs. In Proceedings of the 57th annual meeting of the association for computational linguistics, pages 845-854. Association for Computational Linguistics, 2019. URL: https://doi.org/10.18653/V1/P19-1081.
  118. Deepak Nathani, Jatin Chauhan, Charu Sharma, and Manohar Kaul. Learning attention-based embeddings for relation prediction in knowledge graphs. 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 4710-4723. Association for Computational Linguistics, 2019. URL: https://doi.org/10.18653/V1/P19-1466.
  119. Mojtaba Nayyeri, Gokce Muge Cil, Sahar Vahdati, Francesco Osborne, Mahfuzur Rahman, Simone Angioni, Angelo Salatino, Diego Reforgiato Recupero, Nadezhda Vassilyeva, Enrico Motta, et al. Trans4E: Link prediction on scholarly knowledge graphs. Neurocomputing, 461:530-542, oct 2021. URL: https://doi.org/10.1016/J.NEUCOM.2021.02.100.
  120. Mojtaba Nayyeri, Zihao Wang, Mst Akter, Mirza Mohtashim Alam, Md Rashad Al Hasan Rony, Jens Lehmann, Steffen Staab, et al. Integrating knowledge graph embedding and pretrained language models in hypercomplex spaces. arXiv preprint arXiv:2208.02743, abs/2208.02743, 2022. URL: https://doi.org/10.48550/ARXIV.2208.02743.
  121. Mojtaba Nayyeri, Bo Xiong, Majid Mohammadi, Mst. Mahfuja Akter, Mirza Mohtashim Alam, Jens Lehmann, and Steffen Staab. Knowledge graph embeddings using neural Itô process: From multiple walks to stochastic trajectories. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7165-7179, 2023. URL: https://doi.org/10.18653/V1/2023.FINDINGS-ACL.448.
  122. Thomas Neumann and Guido Moerkotte. Characteristic sets: Accurate cardinality estimation for RDF queries with multiple joins. In 2011 IEEE 27th International Conference on Data Engineering, pages 984-994. IEEE, IEEE, 2011. URL: https://doi.org/10.1109/ICDE.2011.5767868.
  123. Maximilian Nickel, Lorenzo Rosasco, and Tomaso Poggio. Holographic embeddings of knowledge graphs. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 30, pages 1955-1961. Association for the Advancement of Artificial Intelligence (AAAI), 2016. URL: https://doi.org/10.1609/AAAI.V30I1.10314.
  124. Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. A three-way model for collective learning on multi-relational data. In Proceedings of the International Conference on Machine Learning, volume 11, pages 3104482-3104584. Omnipress, 2011. URL: https://icml.cc/2011/papers/438_icmlpaper.pdf.
  125. Matteo Palmonari and Pasquale Minervini. Knowledge graph embeddings and explainable AI. Knowledge Graphs for Explainable Artificial Intelligence: Foundations, Applications and Challenges, 47:49, 2020. URL: https://doi.org/10.3233/SSW200011.
  126. Enrico Palumbo, Diego Monti, Giuseppe Rizzo, Raphaël Troncy, and Elena Baralis. entity2rec: Property-specific knowledge graph embeddings for item recommendation. Expert Systems with Applications, 151:113235, aug 2020. URL: https://doi.org/10.1016/J.ESWA.2020.113235.
  127. Maria Angela Pellegrino, Abdulrahman Altabba, Martina Garofalo, Petar Ristoski, and Michael Cochez. GEval: a modular and extensible evaluation framework for graph embedding techniques. In European Semantic Web Conference, volume 12123, pages 565-582. Springer, Springer International Publishing, 2020. URL: https://doi.org/10.1007/978-3-030-49461-2_33.
  128. Pouya Pezeshkpour, Liyan Chen, and Sameer Singh. Embedding multimodal relational data for knowledge base completion. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, volume abs/1809.01341, pages 3208-3218, Brussels, Belgium, 2018. Association for Computational Linguistics. URL: https://doi.org/10.18653/V1/D18-1359.
  129. Pouya Pezeshkpour, CA Irvine, Yifan Tian, and Sameer Singh. Investigating robustness and interpretability of link prediction via adversarial modifications. In Proceedings of NAACL-HLT, pages 3336-3347. Association for Computational Linguistics, 2019. URL: https://doi.org/10.18653/V1/N19-1337.
  130. R.J. Pittman, Srivastava. Amit, Sanjika Hewavitharana, Ajinkya Kale, and Saab Mansour. Cracking the code on conversational commerce. ebay blog. https://www.ebayinc.com/stories/news/cracking-the-code-on-conversationalcommerce/, 2017. Accessed: 2022-07-03.
  131. 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, 13:399-422, 2022. URL: https://doi.org/10.3233/SW-212892.
  132. 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 abs/2207.06014, pages 592-609. Springer, Springer International Publishing, 2022. URL: https://doi.org/10.1007/978-3-031-19433-7_34.
  133. Jan Portisch and Heiko Paulheim. Walk this way! entity walks and property walks for RDF2vec. CoRR, abs/2204.02777:133-137, 2022. URL: https://doi.org/10.48550/ARXIV.2204.02777.
  134. Kashif Rabbani, Matteo Lissandrini, and Katja Hose. Extraction of validating shapes from very large knowledge graphs. Proceedings of the VLDB Endowment, 16(5):1023-1032, jan 2023. URL: https://doi.org/10.14778/3579075.3579078.
  135. Wessel Radstok, Mel Chekol, and Yannis Velegrakis. Leveraging static models for link prediction in temporal knowledge graphs. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), pages 1034-1041. IEEE, IEEE, nov 2021. URL: https://doi.org/10.1109/ICTAI52525.2021.00165.
  136. Wessel Radstok, Melisachew Wudage Chekol, and Mirko Tobias Schäfer. Are knowledge graph embedding models biased, or is it the data that they are trained on? In Wikidata@ISWC, volume 2982. CEUR-WS.org, 2021. URL: https://ceur-ws.org/Vol-2982/paper-5.pdf.
  137. Yves Raimond, Tristan Ferne, Michael Smethurst, and Gareth Adams. The BBC world service archive prototype. Journal of web semantics, 27:2-9, aug 2014. URL: https://doi.org/10.2139/ssrn.3199103.
  138. Hongyu Ren, Mikhail Galkin, Michael Cochez, Zhaocheng Zhu, and Jure Leskovec. Neural graph reasoning: Complex logical query answering meets graph databases. arXiv.org, abs/2303.14617, mar 2023. URL: https://doi.org/10.48550/ARXIV.2303.14617.
  139. Hongyu Ren, Weihua Hu, and Jure Leskovec. Query2box: Reasoning over knowledge graphs in vector space using box embeddings. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, volume abs/2002.05969. OpenReview.net, feb 2020. URL: https://openreview.net/forum?id=BJgr4kSFDS.
  140. Hongyu Ren and Jure Leskovec. Beta embeddings for multi-hop logical reasoning in knowledge graphs. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, volume abs/2010.11465, 2020. URL: https://proceedings.neurips.cc/paper/2020/hash/e43739bba7cdb577e9e3e4e42447f5a5-Abstract.html.
  141. Petar Ristoski and Heiko Paulheim. Rdf2Vec: RDF graph embeddings for data mining. In Proceedings of the International Semantic Web Conference, volume 9981, pages 498-514. Springer, Springer International Publishing, 2016. URL: https://doi.org/10.1007/978-3-319-46523-4_30.
  142. Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences of the United States of America, 118(15):e2016239118, apr 2021. URL: https://doi.org/10.1073/PNAS.2016239118.
  143. Tim Rocktäschel, Sameer Singh, and Sebastian Riedel. Injecting logical background knowledge into embeddings for relation extraction. In Proceedings of the 2015 conference of the north American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1119-1129. Association for Computational Linguistics, 2015. URL: https://doi.org/10.3115/V1/N15-1118.
  144. Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, and Payel Das. Molformer: Large scale chemical language representations capture molecular structure and properties. Nature Machine Intelligence, 2022. URL: https://doi.org/10.21203/rs.3.rs-1570270/v1.
  145. Tara Safavi and Danai Koutra. CoDEx: A comprehensive knowledge graph completion benchmark. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8328-8350. Association for Computational Linguistics, 2020. URL: https://doi.org/10.18653/V1/2020.EMNLP-MAIN.669.
  146. Hooman Peiro Sajjad, Andrew Docherty, and Yuriy Tyshetskiy. Efficient representation learning using random walks for dynamic graphs. arXiv preprint arXiv:1901.01346, abs/1901.01346, jan 2019. URL: https://doi.org/10.48550/arXiv.1901.01346.
  147. Apoorv Saxena, Adrian Kochsiek, and Rainer Gemulla. Sequence-to-sequence knowledge graph completion and question answering. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume abs/2203.10321, pages 2814-2828, Dublin, Ireland, 2022. Association for Computational Linguistics. URL: https://doi.org/10.18653/V1/2022.ACL-LONG.201.
  148. Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. Modeling relational data with graph convolutional networks. In Proceedings of the European semantic web conference, volume 10843, pages 593-607. Springer International Publishing, 2018. URL: https://doi.org/10.1007/978-3-319-93417-4_38.
  149. Edward W Schneider. Course modularization applied: The interface system and its implications for sequence control and data analysis. In PsycEXTRA Dataset. ERIC, nov 1973. Google Scholar
  150. Lena Schwertmann, Manoj Prabhakar Kannan Ravi, and Gerard de Melo. Model-agnostic bias measurement in link prediction. In Findings of the Association for Computational Linguistics: EACL 2023, Dubrovnik, Croatia, May 2-6, 2023, pages 1587-1603. Association for Computational Linguistics, 2023. URL: https://doi.org/10.18653/V1/2023.FINDINGS-EACL.121.
  151. Pengpeng Shao, Dawei Zhang, Guohua Yang, Jianhua Tao, Feihu Che, and Tong Liu. Tucker decomposition-based temporal knowledge graph completion. Knowledge-Based Systems, 238:107841, feb 2022. URL: https://doi.org/10.1016/J.KNOSYS.2021.107841.
  152. Yinghan Shen, Xuhui Jiang, Zijian Li, Yuanzhuo Wang, Chengjin Xu, Huawei Shen, and Xueqi Cheng. UniSKGRep: A unified representation learning framework of social network and knowledge graph. Neural Networks, 158:142-153, jan 2023. URL: https://doi.org/10.1016/J.NEUNET.2022.11.010.
  153. Baoxu Shi and Tim Weninger. ProjE: Embedding projection for knowledge graph completion. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31, pages 1236-1242. AAAI Press, 2017. URL: https://doi.org/10.1609/AAAI.V31I1.10677.
  154. Khemraj Shukla, Mengjia Xu, Nathaniel Trask, and George E. Karniadakis. Scalable algorithms for physics-informed neural and graph networks. Data-Centric Engineering, 3:e24, 2022. URL: https://doi.org/10.1017/dce.2022.24.
  155. Amit Singhal. Introducing the knowledge graph: things, not strings. Google Blog. https://www.blog.google/products/search/introducing-knowledge-graph-things-not/, 2012. Accessed: 2022-07-03.
  156. Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng. Reasoning with neural tensor networks for knowledge base completion. In Proceedings of the Advances in neural information processing systems, pages 926-934, 2013. URL: https://proceedings.neurips.cc/paper/2013/hash/b337e84de8752b27eda3a12363109e80-Abstract.html.
  157. Ran Song, Shizhu He, Shengxiang Gao, Li Cai, Kang Liu, Zhengtao Yu, and Jun Zhao. Multilingual knowledge graph completion from pretrained language models with knowledge constraints. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7709-7721. Association for Computational Linguistics, 2023. URL: https://doi.org/10.18653/V1/2023.FINDINGS-ACL.488.
  158. 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, pages 697-706. ACM, 2007. URL: https://doi.org/10.1145/1242572.1242667.
  159. Zequn Sun, Qingheng Zhang, Wei Hu, Chengming Wang, Muhao Chen, Farahnaz Akrami, and Chengkai Li. A benchmarking study of embedding-based entity alignment for knowledge graphs. Proc. VLDB Endow., 13(12):2326-2340, jul 2020. URL: https://doi.org/10.14778/3407790.3407828.
  160. Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. Rotate: Knowledge graph embedding by relational rotation in complex space. In Proceedings of the 7th International Conference on Learning Representations. OpenReview.net, feb 2019. URL: https://openreview.net/forum?id=HkgEQnRqYQ.
  161. 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, 2019. URL: https://openreview.net/forum?id=HkgEQnRqYQ.
  162. Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Long-Kai Huang, and Chi Xu. Recurrent knowledge graph embedding for effective recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems, pages 297-305. ACM, 2018. URL: https://doi.org/10.1145/3240323.3240361.
  163. Xiaoli Tang, Rui Yuan, Qianyu Li, Tengyun Wang, Haizhi Yang, Yundong Cai, and Hengjie Song. Timespan-aware dynamic knowledge graph embedding by incorporating temporal evolution. IEEE Access, 8:6849-6860, jan 2020. URL: https://doi.org/10.1109/ACCESS.2020.2964028.
  164. Xing Tang, Ling Chen, Jun Cui, and Baogang Wei. Knowledge representation learning with entity descriptions, hierarchical types, and textual relations. Information Processing & Management, 56(3):809-822, may 2019. URL: https://doi.org/10.1016/J.IPM.2019.01.005.
  165. Yi Tay, Anh Luu, and Siu Cheung Hui. Non-parametric estimation of multiple embeddings for link prediction on dynamic knowledge graphs. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31, pages 1243-1249. Association for the Advancement of Artificial Intelligence (AAAI), 2017. URL: https://doi.org/10.1609/AAAI.V31I1.10685.
  166. Yi Tay, Luu Anh Tuan, Minh C Phan, and Siu Cheung Hui. Multi-task neural network for non-discrete attribute prediction in knowledge graphs. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 1029-1038. ACM, 2017. URL: https://doi.org/10.1145/3132847.3132937.
  167. Komal Teru, Etienne Denis, and Will Hamilton. Inductive relation prediction by subgraph reasoning. In International Conference on Machine Learning, volume 119, pages 9448-9457. PMLR, PMLR, 2020. URL: http://proceedings.mlr.press/v119/teru20a.html.
  168. Vinh Tong, Dat Quoc Nguyen, Trung Thanh Huynh, Tam Thanh Nguyen, Quoc Viet Hung Nguyen, and Mathias Niepert. Joint multilingual knowledge graph completion and alignment. In Findings of the Association for Computational Linguistics: EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, pages 4646-4658. Association for Computational Linguistics, 2022. URL: https://doi.org/10.18653/V1/2022.FINDINGS-EMNLP.341.
  169. Kristina Toutanova, Danqi Chen, Patrick Pantel, Hoifung Poon, Pallavi Choudhury, and Michael Gamon. Representing text for joint embedding of text and knowledge bases. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1499-1509. Association for Computational Linguistics, 2015. URL: https://doi.org/10.18653/V1/D15-1174.
  170. Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. Complex embeddings for simple link prediction. In International conference on machine learning, volume 48, pages 2071-2080. PMLR, JMLR.org, 2016. URL: http://proceedings.mlr.press/v48/trouillon16.html.
  171. Neil Veira, Brian Keng, Kanchana Padmanabhan, and Andreas G Veneris. Unsupervised embedding enhancements of knowledge graphs using textual associations. In IJCAI, pages 5218-5225. International Joint Conferences on Artificial Intelligence Organization, 2019. URL: https://doi.org/10.24963/IJCAI.2019/725.
  172. Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. Graph attention networks. In International Conference on Learning Representations. OpenReview.net, 2018. URL: https://openreview.net/forum?id=rJXMpikCZ.
  173. Denny Vrandečić and Markus Krötzsch. Wikidata: a free collaborative knowledgebase. Communications of the ACM, 57:78-85, 2014. URL: https://doi.org/10.1145/2629489.
  174. Bo Wang, Tao Shen, Guodong Long, Tianyi Zhou, Ying Wang, and Yi Chang. Structure-augmented text representation learning for efficient knowledge graph completion. In Proceedings of the Web Conference 2021, pages 1737-1748. ACM, 2021. URL: https://doi.org/10.1145/3442381.3450043.
  175. Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. RippleNet: Propagating user preferences on the knowledge graph for recommender systems. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pages 417-426. ACM, mar 2018. URL: https://doi.org/10.1145/3269206.3271739.
  176. Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. DKN: Deep knowledge-aware network for news recommendation. In Proceedings of the 2018 world wide web conference, pages 1835-1844. ACM, 2018. URL: https://doi.org/10.1145/3178876.3186175.
  177. Jingbin Wang, Wang Zhang, Xinyuan Chen, Jing Lei, and Xiaolian Lai. 3DRTE: 3d rotation embedding in temporal knowledge graph. IEEE Access, 8:207515-207523, 2020. URL: https://doi.org/10.1109/ACCESS.2020.3036897.
  178. Rui Wang, Bicheng Li, Shengwei Hu, Wenqian Du, and Min Zhang. Knowledge graph embedding via graph attenuated attention networks. IEEE Access, 8:5212-5224, 2019. URL: https://doi.org/10.1109/ACCESS.2019.2963367.
  179. 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, pages 950-958. ACM, 2019. URL: https://doi.org/10.1145/3292500.3330989.
  180. Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, and Tat-Seng Chua. Explainable reasoning over knowledge graphs for recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 5329-5336. Association for the Advancement of Artificial Intelligence (AAAI), 2019. URL: https://doi.org/10.1609/AAAI.V33I01.33015329.
  181. Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhengyan Zhang, Zhiyuan Liu, Juanzi Li, and Jian Tang. KEPLER: A unified model for knowledge embedding and pre-trained language representation. Transactions of the Association for Computational Linguistics, 9:176-194, 2021. URL: https://doi.org/10.1162/TACL_A_00360.
  182. Yashen Wang and Huanhuan Zhang. HARP: A novel hierarchical attention model for relation prediction. ACM Transactions on Knowledge Discovery from Data (TKDD), 15:1-22, 2021. URL: https://doi.org/10.1145/3424673.
  183. Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. Knowledge graph and text jointly embedding. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1591-1601, Doha, Qatar, 2014. Association for Computational Linguistics. URL: https://doi.org/10.3115/V1/D14-1167.
  184. Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. Knowledge graph embedding by translating on hyperplanes. In Proceedings of the AAAI conference on artificial intelligence, volume 28, pages 1112-1119. Association for the Advancement of Artificial Intelligence (AAAI), 2014. URL: https://doi.org/10.1609/AAAI.V28I1.8870.
  185. Zhihao Wang and Xin Li. Hybrid-TE: Hybrid translation-based temporal knowledge graph embedding. In 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pages 1446-1451. IEEE, IEEE, 2019. URL: https://doi.org/10.1109/ICTAI.2019.00205.
  186. Yuyang Wei, Wei Chen, Zhixu Li, and Lei Zhao. Incremental update of knowledge graph embedding by rotating on hyperplanes. In 2021 IEEE International Conference on Web Services (ICWS), pages 516-524. IEEE, 2021. URL: https://doi.org/10.1109/ICWS53863.2021.00072.
  187. Christopher Wewer, Florian Lemmerich, and Michael Cochez. Updating embeddings for dynamic knowledge graphs. arXiv.org, abs/2109.10896, sep 2021. URL: https://doi.org/10.48550/arXiv.2109.10896.
  188. Jiawei Wu, Ruobing Xie, Zhiyuan Liu, and Maosong Sun. Knowledge representation via joint learning of sequential text and knowledge graphs. arXiv preprint arXiv:1609.07075, abs/1609.07075, sep 2016. URL: https://doi.org/10.48550/arXiv.1609.07075.
  189. Tianxing Wu, Arijit Khan, Melvin Yong, Guilin Qi, and Meng Wang. Efficiently embedding dynamic knowledge graphs. Knowledge-Based Systems, 250:109124, aug 2022. URL: https://doi.org/10.1016/J.KNOSYS.2022.109124.
  190. Yanrong Wu and Zhichun Wang. Knowledge graph embedding with numeric attributes of entities. In Proceedings of the Rep4NLP@ACL, pages 132-136. Association for Computational Linguistics, 2018. URL: https://doi.org/10.18653/V1/W18-3017.
  191. Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, and Yongfeng Zhang. Reinforcement knowledge graph reasoning for explainable recommendation. In Proceedings of SIGIR 2019, pages 285-294, 2019. URL: https://doi.org/10.1145/3331184.3331203.
  192. Han Xiao, Minlie Huang, Yu Hao, and Xiaoyan Zhu. TransG: A generative mixture model for knowledge graph embedding. arXiv preprint arXiv:1509.05488, abs/1509.05488, 2015. URL: https://doi.org/10.48550/arXiv.1509.05488.
  193. Ruobing Xie, Zhiyuan Liu, Huanbo Luan, and Maosong Sun. Image-embodied knowledge representation learning. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, pages 3140-3146. International Joint Conferences on Artificial Intelligence Organization, 2017. URL: https://doi.org/10.24963/IJCAI.2017/438.
  194. Ruobing Xie, Zhiyuan Liu, Maosong Sun, et al. Representation learning of knowledge graphs with hierarchical types. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 2965-2971. IJCAI/AAAI Press, 2016. URL: http://www.ijcai.org/Abstract/16/421.
  195. Chengjin Xu, Yung-Yu Chen, Mojtaba Nayyeri, and Jens Lehmann. Temporal knowledge graph completion using a linear temporal regularizer and multivector embeddings. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2569-2578. Association for Computational Linguistics, 2021. URL: https://doi.org/10.18653/V1/2021.NAACL-MAIN.202.
  196. Jiacheng Xu, Xipeng Qiu, Kan Chen, and Xuanjing Huang. Knowledge graph representation with jointly structural and textual encoding. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, pages 1318-1324. International Joint Conferences on Artificial Intelligence Organization, aug 2017. URL: https://doi.org/10.24963/IJCAI.2017/183.
  197. Youri Xu, E Haihong, Meina Song, Wenyu Song, Xiaodong Lv, Wang Haotian, and Yang Jinrui. RTFE: A recursive temporal fact embedding framework for temporal knowledge graph completion. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5671-5681. Association for Computational Linguistics, 2021. URL: https://doi.org/10.18653/V1/2021.NAACL-MAIN.451.
  198. Bishan Yang, Wen tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. Embedding entities and relations for learning and inference in knowledge bases. In Proceedings of the 3rd International Conference on Learning Representations, 2015. URL: http://arxiv.org/abs/1412.6575.
  199. Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575, dec 2014. URL: https://doi.org/10.48550/arXiv.1412.6575.
  200. Liang Yao, Chengsheng Mao, and Yuan Luo. KG-BERT: BERT for knowledge graph completion. CoRR, abs/1909.03193, sep 2019. URL: https://doi.org/10.48550/arXiv.1909.03193.
  201. Siyu Yao, Ruijie Wang, Shen Sun, Derui Bu, and Jun Liu. Joint embedding learning of educational knowledge graphs. Artificial Intelligence Supported Educational Technologies, pages 209-224, 2020. URL: https://doi.org/10.1007/978-3-030-41099-5_12.
  202. Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 974-983. ACM, 2018. URL: https://doi.org/10.1145/3219819.3219890.
  203. Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. GNNExplainer: Generating explanations for graph neural networks. Advances in neural information processing systems, 32:9240-9251, 2019. URL: https://proceedings.neurips.cc/paper/2019/hash/d80b7040b773199015de6d3b4293c8ff-Abstract.html.
  204. Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou, Jun Zhao, Zheng Liu, Jing Li, and Maosong Sun. Recursive neural networks for complex relation extraction. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 945-951, 2014. Google Scholar
  205. Chuxu Zhang, Huaxiu Yao, Lu Yu, Chao Huang, Dongjin Song, Haifeng Chen, Meng Jiang, and Nitesh V Chawla. Inductive contextual relation learning for personalization. ACM Transactions on Information Systems (TOIS), 39(3):1-22, jul 2021. URL: https://doi.org/10.1145/3450353.
  206. Fuxiang Zhang, Xin Wang, Zhao Li, and Jianxin Li. TransRHS: a representation learning method for knowledge graphs with relation hierarchical structure. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pages 2987-2993, 2021. URL: https://doi.org/10.24963/IJCAI.2020/413.
  207. Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 353-362. ACM, 2016. URL: https://doi.org/10.1145/2939672.2939673.
  208. Hanwang Zhang, Zawlin Kyaw, Shih-Fu Chang, and Tat-Seng Chua. Visual translation embedding network for visual relation detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5532-5540. IEEE, 2017. URL: https://doi.org/10.1109/CVPR.2017.331.
  209. Wen Zhang, Jiaoyan Chen, Juan Li, Zezhong Xu, Jeff Z Pan, and Huajun Chen. Knowledge graph reasoning with logics and embeddings: Survey and perspective. arXiv preprint arXiv:2202.07412, abs/2202.07412, 2022. URL: https://doi.org/10.48550/arXiv.2202.07412.
  210. Wen Zhang, Shumin Deng, Mingyang Chen, Liang Wang, Qiang Chen, Feiyu Xiong, Xiangwen Liu, and Huajun Chen. Knowledge graph embedding in e-commerce applications: Attentive reasoning, explanations, and transferable rules. In Proceedings of the 10th International Joint Conference on Knowledge Graphs, pages 71-79. ACM, 2021. URL: https://doi.org/10.1145/3502223.3502232.
  211. Zhanqiu Zhang, Jianyu Cai, Yongdong Zhang, and Jie Wang. Learning hierarchy-aware knowledge graph embeddings for link prediction. In AAAI, volume 34, pages 3065-3072. AAAI Press, apr 2020. URL: https://doi.org/10.1609/AAAI.V34I03.5701.
  212. Zhao Zhang, Fuzhen Zhuang, Meng Qu, Fen Lin, and Qing He. Knowledge graph embedding with hierarchical relation structure. In EMNLP, pages 3198-3207. Association for Computational Linguistics, 2018. URL: https://doi.org/10.18653/V1/D18-1358.
  213. Zhiyuan Zhang, Xiaoqian Liu, Yi Zhang, Qi Su, Xu Sun, and Bin He. Pretrain-KGE: learning knowledge representation from pretrained language models. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 259-266. Association for Computational Linguistics, 2020. URL: https://doi.org/10.18653/V1/2020.FINDINGS-EMNLP.25.
  214. Cunchao Zhu, Muhao Chen, Changjun Fan, Guangquan Cheng, and Yan Zhang. Learning from history: Modeling temporal knowledge graphs with sequential copy-generation networks. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 4732-4740. Association for the Advancement of Artificial Intelligence (AAAI), 2021. URL: https://doi.org/10.1609/AAAI.V35I5.16604.
  215. Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, and Jian Tang. Neural bellman-ford networks: A general graph neural network framework for link prediction. Advances in Neural Information Processing Systems, 34:29476-29490, 2021. URL: https://proceedings.neurips.cc/paper/2021/hash/f6a673f09493afcd8b129a0bcf1cd5bc-Abstract.html.
  216. Amal Zouaq and Felix Martel. What is the schema of your knowledge graph? leveraging knowledge graph embeddings and clustering for expressive taxonomy learning. In Proceedings of the international workshop on semantic big data, pages 1-6. ACM, 2020. URL: https://doi.org/10.1145/3391274.3393637.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail