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research-article

Temporal knowledge graph embedding via sparse transfer matrix

Authors:

Huanhuan ChenAuthors Info & Claims

Volume 623, Issue C

Pages 56 - 69

https://doi.org/10.1016/j.ins.2022.12.019

Published: 01 April 2023 Publication History

Abstract

Knowledge Graph Completion (KGC) is a fundamental problem for temporal knowledge graphs (TKGs), and TKGs embedding methods are one of the essential methods for KGC. However, existing TKG embedding methods encounter a scalability dilemma, i.e., the inconsistency in parameter scalability among different datasets, and the less use of global information, e.g., statistics and dependencies of facts. To mitigate these two issues, we propose a novel and effective TKG embedding method, named T emporal Knowledge Gr a ph Embedding via S parse T ransf e r Mat r ix (TASTER), which provides a framework to utilize both global and local information. Regarding a TKG as a static knowledge graph when ignoring the time dimension, TASTER first learns global embeddings based on this static knowledge graph to capture global information. To capture the local information in a specific timestamp, TASTER evolves local embeddings from global embeddings based on the corresponding subgraph. Besides, TASTER learns evolving entity embeddings through sparse transformation matrices, which could better adapt to TKGs with a varied number of subgraphs. We conduct experiments on three real-world datasets, and TASTER outperforms most existing models on the link prediction task of TKGs, which validates the its effectiveness.

References

[1]

Elizabeth Boschee, Jennifer Lautenschlager, Sean O’Brien, Steve Shellman, James Starz, and Michael Ward. ICEWS Coded Event Data. Harvard Dataverse, 12, 2015.

[2]

Shib Sankar Dasgupta, Swayambhu Nath Ray, Partha Talukdar, HyTE: Hyperplane-based Temporally Aware Knowledge Graph Embedding, in: Proceedings of the 2018 conference on empirical methods in natural language processing, 2018, pp. 2001–2011.

[3]

Kalev Leetaru and Philip A Schrodt. Gdelt: Global Data on Events, Location, and Tone, 1979–2012. In ISA Annual Convention, pages 1–49, 2013.

[4]

Arpita Chaudhuri, Monalisa Sarma, Debasis Samanta, Share: Designing multiple criteria-based personalized research paper recommendation system, Inf. Sci. 617 (2022) 41–64.

[5]

Jiaying Chen, Yu. Jiong, Lu. Wenjie, Yurong Qian, Ping Li, IR-Rec: An Interpretive Rules-guided Recommendation over Knowledge Graph, Inf. Sci. 563 (2021) 326–341.

[6]

Hai Cui, Tao Peng, Feng Xiao, Jiayu Han, Ridong Han, Lu Liu, Incorporating anticipation embedding into reinforcement learning framework for multi-hop knowledge graph question answering, Inf. Sci. (2022).

[7]

Xinyan Zhao, Feng Xiao, Haoming Zhong, Jun Yao, and Huanhuan Chen. Condition Aware and Revise Transformer for Question Answering. In Proceedings of the Web Conference 2020, pages 2377–2387, 2020.

[8]

Shengfei Lyu, Wu. Xingyu, Jinlong Li, Qiuju Chen, Huanhuan Chen, Do Models Learn the Directionality of Relations? A New Evaluation: Relation Direction Recognition, in: IEEE Transactions on Emerging Topics in Computational Intelligence, 2022, pp. 1–10.

[9]

Gangqiang Hu, Shengfei Lyu, Xingyu Wu, Jinlong Li, and Huanhuan Chen. Contextual-Aware Information Extractor with Adaptive Objective for Chinese Medical Dialogues. ACM Transactions on Asian and Low-Resource Language Information Processing, Jan 2022.

[10]

Shengfei Lyu, Huanhuan Chen, Relation Classification with Entity Type Restriction, in: Proceedings of the Findings of the Association for Computational Linguistics: ACL-IJCNLP, 2021, 2021, pp. 390–395.

[11]

Shengfei Lyu, Jin Cheng, Xingyu Wu, Lizhen Cui, Huanhuan Chen, and Chunyan Miao. Auxiliary Learning for Relation Extraction. IEEE Transactions on Emerging Topics in Computational Intelligence, 2020.

[12]

Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, S. Yu Philip, A Survey on Knowledge Graphs: Representation, Acquisition, and Applications, IEEE Trans. Neural Networks Learn. Syst. (2021).

[13]

Alberto Garcia-Duran, Sebastijan Dumančić, Mathias Niepert, Learning Sequence Encoders for Temporal Knowledge Graph Completion, in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018, pp. 4816–4821.

[14]

Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, Pascal Poupart, Diachronic Embedding for Temporal Knowledge Graph Completion, in: Processing of the 34th AAAI Conference on Artificial Intelligence, 2020, pp. 3988–3995.

[15]

Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, and Jens Lehmann. TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1583–1593, 2020.

[16]

Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. Translating Embeddings for Modeling Multi-relational Data. In Proceedings of the Annual Conference on Neural Information Processing Systems 2013, pages 2787–2795, 2013.

[17]

Xu. Jian, Jiawei Liu, Liangang Zhang, Zhengyu Li, Huanhuan Chen, Improve Chinese Word Embeddings by Exploiting Internal Structure, in: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, 2016, pp. 1041–1050.

[18]

Jiawei Liu, Zhenyu Liu, Huanhuan Chen, Revisit Word Embeddings with Semantic Lexicons for Modeling Lexical Contrast, in: 2017 IEEE International Conference on Big Knowledge (ICBK), 2017, pp. 72–79.

[19]

Xuejiao Zhao, Huanhuan Chen, Zhenchang Xing, Chunyan Miao, Brain-Inspired Search Engine Assistant Based on Knowledge Graph, in: IEEE Transaction Neural Networks and Learning Systems, 2021, pp. 1–15.

[20]

Xiangyu Wang, Lyuzhou Chen, Taiyu Ban, Muhammad Usman, Yifeng Guan, Shikang Liu, Wu. Tianhao, Huanhuan Chen, Knowledge Graph Quality Control: A Survey, Fundam. Res. 1 (5) (2021) 607–626.

[21]

Wu. Xingyu, Zhenchao Tao, Bingbing Jiang, Wu. Tianhao, Xin Wang, Huanhuan Chen, Domain knowledge-enhanced variable selection for biomedical data analysis, Inf. Sci. 606 (2022) 469–488.

[22]

Qi Xiao, Yunchuan Qin, Kenli Li, Zhuo Tang, Fan Wu, and Zhizhong Liu. An unsupervised semantic text similarity measurement model in resource-limited scenes. Information Sciences, 2022.

[23]

Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen, Knowledge Graph Embedding by Translating on Hyperplanes, in: Proceedings of the 28th AAAI Conference on Artificial Intelligence, 2014, pp. 1112–1119.

[24]

Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. Learning Entity and Relation Embeddings for Knowledge Graph Completion. In Proceedings of the 29th AAAI Conference on Artificial Intelligence, pages 2181–2187, 2015.

[25]

Han Xiao, Minlie Huang, Xiaoyan Zhu, From One Point to a Manifold: Knowledge Graph Embedding for Precise Link Prediction, in: Proceedings of the 25th International Joint Conference on Artificial Intelligence, 2015, pp. 1315–1321.

[26]

Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang, RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space, in: Processing of the 7th International Conference on Learning Representations, 2018.

[27]

Zhanqiu Zhang, Jianyu Cai, Yongdong Zhang, Jie Wang, Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction, in: Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020, pp. 3065–3072.

[28]

Dinesh Garg, Shajith Ikbal, Santosh K. Srivastava, Harit Vishwakarma, Hima Karanam, L. Venkata Subramaniam, Quantum Embedding of Knowledge for Reasoning, Advances in Neural Information Processing Systems 32 (2019) 5594–5604.

[29]

Quan Wang, Zhendong Mao, Bin Wang, Li Guo, Knowledge Graph Embedding: A Survey of Approaches and Applications, IEEE Trans. Knowl. Data Eng. 29 (12) (2017) 2724–2743.

[30]

Bishan Yang, Scott 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.

[31]

Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard, Complex Embeddings for Simple Link Prediction, in: Proceedings of the 33nd International Conference on Machine Learning, 2016, pp. 2071–2080.

[32]

Canran Xu and Ruijiang Li. Relation Embedding with Dihedral Group in Knowledge Graph. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 263–272, 2019.

[33]

Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio, Holographic Embeddings of Knowledge Graphs, in: Proceedings of the 30th AAAI Conference on Artificial Intelligence, 2016, pp. 1955–1961.

[34]

Yongming Han, Guofei Chen, Zhongkun Li, Zhiqiang Geng, Fang Li, Bo Ma, An Asymmetric Knowledge Representation Learning in Manifold Space, Inf. Sci. 531 (2020) 1–12.

[35]

Tingsong Jiang, Tianyu Liu, Tao Ge, Lei Sha, Baobao Chang, Sujian Li, and Zhifang Sui. Towards Time-aware Knowledge Graph Completion. In Proceedings of the 26th International Conference on Computational Linguistics, pages 1715–1724, 2016.

[36]

Timothée Lacroix, Guillaume Obozinski, Nicolas Usunier, Tensor Decompositions for Temporal Knowledge Base Completion, in: Processing of the 8th International Conference on Learning Representations, 2019.

[37]

Xu. Chenjin, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Yazdi, Jens Lehmann, Temporal knowledge graph completion based on time series gaussian embedding, in: Processing of the 19th International Semantic Web Conference, 2020, pp. 654–671.

[38]

Ali Sadeghian, Miguel Rodriguez, Daisy Zhe Wang, Anthony Colas, Temporal Reasoning over Event Knowledge Graphs, Workshop on Knowledge Base Construction, Reasoning and Mining (2016).

[39]

Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song, Know-evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs, in: Proceedings of the 34th International Conference on Machine Learning, 2017, pp. 3462–3471.

[40]

Yousef Saad, Iterative Methods for Sparse Linear Systems, Society for Industrial and Applied Mathematics (2003).

[41]

Guoliang Ji, Kang Liu, Shizhu He, Jun Zhao, Knowledge Graph Completion with Adaptive Sparse Transfer Matrix, in: Proceedings of the 30th AAAI Conference on Artificial Intelligence, 2016, pp. 985–991.

[42]

Seyed Mehran Kazemi, David Poole, SimplE Embedding for Link Prediction in Knowledge Graphs, in: Proceedings of the Annual Conference on Neural Information Processing Systems, 2018, 2018, pp. 4289–4300.

[43]

Rudolf Kadlec, Ondřej Bajgar, Jan Kleindienst, Knowledge Base Completion: Baselines Strike Back, in: Proceedings of the 2nd Workshop on Representation Learning for NLP, 2017, pp. 69–74.

[44]

Fredo Erxleben, Michael Günther, Markus Krötzsch, Julian Mendez, and Denny Vrandečić. Introducing Wikidata to the Linked Data Web. In Proceedings of the 13th International Semantic Web Conference, pages 50–65, 2014.

[45]

Adam Paszke, Sam Gross, Francisco Massa, et al., Pytorch: An imperative style, high-performance deep learning library, in: Proceedings of the Annual Conference on Neural Information Processing Systems, 2019, 2019, pp. 8024–8035.

Cited By

Mei XYang LJiang ZCai XGao DHan JPan S(2024)An Inductive Reasoning Model based on Interpretable Logical Rules over temporal knowledge graphNeural Networks10.1016/j.neunet.2024.106219174:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106219
Chen YMensah SLi J(2024)Learning distributed representations of knowledge that preserve deductive reasoningKnowledge-Based Systems10.1016/j.knosys.2024.111635293:COnline publication date: 7-Jun-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111635
Ma JLi KZhang FWang YLuo XLi CQiao Y(2024)TaReTInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10384861:6Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103848
Show More Cited By

Index Terms

Temporal knowledge graph embedding via sparse transfer matrix
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 623, Issue C

Apr 2023

932 pages

ISSN:0020-0255

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Elsevier Inc.

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Elsevier Science Inc.

United States

Publication History

Published: 01 April 2023

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Cited By

Mei XYang LJiang ZCai XGao DHan JPan S(2024)An Inductive Reasoning Model based on Interpretable Logical Rules over temporal knowledge graphNeural Networks10.1016/j.neunet.2024.106219174:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106219
Chen YMensah SLi J(2024)Learning distributed representations of knowledge that preserve deductive reasoningKnowledge-Based Systems10.1016/j.knosys.2024.111635293:COnline publication date: 7-Jun-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111635
Ma JLi KZhang FWang YLuo XLi CQiao Y(2024)TaReTInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10384861:6Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103848
Qian YSun FWang XPan L(2024)TODEARInformation Sciences: an International Journal10.1016/j.ins.2024.121066679:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.121066
Du CLi XLi Z(2024)Semantic-enhanced reasoning question answering over temporal knowledge graphsJournal of Intelligent Information Systems10.1007/s10844-024-00840-562:3(859-881)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10844-024-00840-5
Xie RRuan KHuang BYu WXiao JHuang J(2024)TSA-Net: a temporal knowledge graph completion method with temporal-structural adaptationApplied Intelligence10.1007/s10489-024-05734-154:21(10320-10332)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s10489-024-05734-1
Gao CZhou ZShi LOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Noether embeddingProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668213(48185-48202)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668213

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