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Global and local information-aware relational graph convolutional network for temporal knowledge graph completion

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Abstract

Temporal knowledge graph completion (TKGC) focuses on inferring missing facts from temporal knowledge graphs (TKGs) and has been widely studied. While previous models based on graph neural networks (GNNs) have shown noteworthy outcomes, they tend to focus on designing complex modules to learn contextual representations. These complex solutions require a large number of parameters and heavy memory consumption. Additionally, existing TKGC approaches focus on exploiting static feature representation for entities and relationships, which fail to effectively capture the semantic information of contexts. In this paper, we propose a global and local information-aware relational graph convolutional neural network (GLARGCN) model to address these issues. First, we design a sampler, which captures significant neighbors by combining global historical event frequencies with local temporal relative displacements and requires no additional learnable parameters. We then employ a time-aware encoder to model timestamps, relations, and entities uniformly. We perform a graph convolution operation to learn a global graph representation. Finally, our method predicts missing entities using a scoring function. We evaluate the model on four benchmark datasets and one specific dataset with unseen timestamps. The experimental results demonstrate that our proposed GLARGCN model not only outperforms contemporary models but also shows robust performance in scenarios with unseen timestamps.

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Data Availability

The dataset on ICEWS14 and ICEWS05-15 are openly available at https://doi.org/10.7910/DVN/28075. The dataset on GDELT is openly available at https://dl.acm.org/doi/10.5555/3305890.3306039. The dataset on Wikidata12k is openly available at https://doi.org/10.18653/v1/2021.naacl-main.202.

Materials Availability

Not applicable

Code Availability

The code that supports the findings of this study is available on request from the corresponding author.

References

  1. Yang C, Qi G (2022) An urban traffic knowledge graph-driven spatial-temporal graph convolutional network for traffic flow prediction. In: Proceedings of the 11th International Joint Conference on Knowledge Graphs, pp 110–114. https://doi.org/10.1145/3579051.3579058

  2. Zhang W, Gu T, Sun W, et al (2018) Travel attractions recommendation with travel spatial-temporal knowledge graphs. In: Proceedings of the International Conference of Pioneering Computer Scientists, Engineers and Educators, pp 213–226. https://doi.org/10.1007/978-981-13-2206-8_19

  3. Souza Costa T, Gottschalk S, Demidova E (2020) Event-qa: A dataset for event-centric question answering over knowledge graphs. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp 3157–3164. https://doi.org/10.1145/3340531.3412760

  4. Trompf GW (1979) The idea of historical recurrence in Western thought: from antiquity to the Reformation, vol 1. Univ of California Press

  5. Schlesinger AM (1999) The cycles of American history. HMH

  6. Zhu C, Chen M, Fan C et al (2021) Learning from history: Modeling temporal knowledge graphs with sequential copy-generation networks. Proceed AAAI Conf Art Intell 35:4732–4740. https://doi.org/10.1609/aaai.v35i5.16604

    Article  MATH  Google Scholar 

  7. Ding Z, Ma Y, He B, et al (2022) A simple but powerful graph encoder for temporal knowledge graph completion. In: NeurIPS 2022 Temporal Graph Learning Workshop. https://openreview.net/forum?id=DYG8RbgAIo

  8. Jung J, Jung J, Kang U (2021) Learning to walk across time for interpretable temporal knowledge graph completion. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp 786–795. https://doi.org/10.1145/3447548.3467292

  9. Xie Z, Zhu R, Liu J, et al (2023) Targat: A time-aware relational graph attention model for temporal knowledge graph embedding. IEEE/ACM Trans Audio, Speech, Language Process pp 2246–2258. https://doi.org/10.1109/TASLP.2023.3282101

  10. Bordes A, Usunier N, Garcia-Duran A, et al (2013) Translating embeddings for modeling multi-relational data. Adv Condens Matter Phys 26

  11. Wang Z, Zhang J, Feng J, et al (2014) Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI conference on artificial intelligence, vol 28. https://doi.org/10.1609/aaai.v28i1.8870

  12. Lin Y, Liu Z, Sun M, et al (2015) Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the AAAI conference on artificial intelligence, vol 29

  13. Sun Z, Deng ZH, Nie JY, et al (2019) Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv:1902.10197

  14. Zhang S, Tay Y, Yao L, et al (2019) Quaternion knowledge graph embeddings. In: Advances in Neural Information Processing Systems, vol 32

  15. Nickel M, Tresp V, Kriegel HP (2011) A three-way model for collective learning on multi-relational data. In: International Conference on Machine Learning, pp 809—-816

  16. Yang B, Yih SWt, He X, et al (2015) Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the International Conference on Learning Representations (ICLR)

  17. Trouillon T, Welbl J, Riedel S, et al (2016) Complex embeddings for simple link prediction. In: Proceedings of The 33rd International Conference on Machine Learning, vol 48, pp 2071–2080

  18. Dettmers T, Minervini P, Stenetorp P, et al (2018) Convolutional 2d knowledge graph embeddings. In: Proceedings of the AAAI conference on artificial intelligence, vol 32 .https://doi.org/10.1609/aaai.v32i1.11573

  19. Nguyen DQ, Nguyen TD, Nguyen DQ, et al (2018) 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, Volume 2 (Short Papers), pp 327–333. https://doi.org/10.18653/v1/N18-2053

  20. Schlichtkrull M, Kipf TN, Bloem P, et al (2018) Modeling relational data with graph convolutional networks. In: The semantic web: 15th international conference, pp 593–607. https://doi.org/10.1007/978-3-319-93417-4_38

  21. Bansal T, Juan DC, Ravi S, et al (2019) A2n: Attending to neighbors for knowledge graph inference. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 4387–4392https://doi.org/10.18653/v1/P19-1431

  22. Qin X, Sheikh N, Reinwald B et al (2021) Relation-aware graph attention model with adaptive self-adversarial training. Proceed AAAI Conf Art Intell 35:9368–9376. https://doi.org/10.1609/aaai.v35i11.17129

    Article  Google Scholar 

  23. Jiang T, Liu T, Ge T, et al (2016) Towards time-aware knowledge graph completion. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp 1715–1724

  24. Dasgupta SS, Ray SN, Talukdar P (2018) Hyte: Hyperplane-based temporally aware knowledge graph embedding. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 2001–2011. https://doi.org/10.18653/v1/D18-1225

  25. Xu C, Chen YY, Nayyeri M, et al (2021) 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, pp 2569–2578.https://doi.org/10.18653/v1/2021.naacl-main.202

  26. Leblay J, Chekol MW (2018) Deriving validity time in knowledge graph. Comp Proceed the Web Conf 2018:1771–1776. https://doi.org/10.1145/3184558.3191639

    Article  MATH  Google Scholar 

  27. García-Durán A, Dumančić S, Niepert M (2018) Learning sequence encoders for temporal knowledge graph completion. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp 4816–4821. https://doi.org/10.18653/v1/D18-1516

  28. Wu J, Cao M, Cheung JCK, et al (2020) Temp: Temporal message passing for temporal knowledge graph completion. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 5730–5746. https://doi.org/10.18653/v1/2020.emnlp-main.462

  29. Zhang F, Zhang Z, Ao X, et al (2022) Along the time: timeline-traced embedding for temporal knowledge graph completion. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp 2529–2538. https://doi.org/10.1145/3511808.3557233

  30. Dikeoulias I, Amin S, Neumann G (2022) Temporal knowledge graph reasoning with low-rank and model-agnostic representations. In: Proceedings of the 7th Workshop on Representation Learning for NLP, pp 111–120. https://doi.org/10.18653/v1/2022.repl4nlp-1.12

  31. Kazemi SM, Poole D (2018) Simple embedding for link prediction in knowledge graphs. In: Bengio S, Wallach H, Larochelle H, et al (eds) Advances in Neural Information Processing Systems, vol 31

  32. Boschee E, Lautenschlager J, O’Brien S et al (2015). ICEWS Coded Event Data. https://doi.org/10.7910/DVN/28075

    Article  Google Scholar 

  33. Leetaru K, Schrodt PA (2013) Gdelt: Global data on events, location, and tone, 1979–2012. ISA Annual Conv, Citeseer 2:1–49

    Google Scholar 

  34. Erxleben F, Günther M, Krötzsch M, et al (2014) Introducing wikidata to the linked data web. In: The Semantic Web–ISWC 2014: 13th International Semantic Web Conference, pp 50–65. https://doi.org/10.1007/978-3-319-11964-9_4

  35. Trivedi R, Dai H, Wang Y, et al (2017) Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. In: Proceedings of the 34th International Conference on Machine Learning, Proceedings of Machine Learning Research, vol 70, pp 3462–3471. https://doi.org/10.5555/3305890.3306039

  36. Goel R, Kazemi SM, Brubaker M et al (2020) Diachronic embedding for temporal knowledge graph completion. Proceed AAAI Conf Art Intell 34:3988–3995. https://doi.org/10.1609/aaai.v34i04.5815

    Article  MATH  Google Scholar 

  37. Xu C, Nayyeri M, Alkhoury F, et al (2020) Temporal knowledge graph completion based on time series gaussian embedding. In: The Semantic Web–ISWC 2020: 19th International Semantic Web Conference, Athens, Greece, 2–6 November, 2020, Proceedings, Part I 19, pp 654–671. https://doi.org/10.1007/978-3-030-62419-4_37

  38. Lacroix T, Obozinski G, Usunier N (2020) Tensor decompositions for temporal knowledge base completion. arXiv:2004.04926

  39. Sadeghian A, Armandpour M, Colas A et al (2021) Chronor: Rotation based temporal knowledge graph embedding. Proceed AAAI Conf Art Intell 35:6471–6479. https://doi.org/10.1609/aaai.v35i7.16802

    Article  Google Scholar 

  40. Messner J, Abboud R, Ceylan II (2022) Temporal knowledge graph completion using box embeddings. Proceed AAAI Conf Art Intell 36:7779–778. https://doi.org/10.1609/aaai.v36i7.20746

    Article  Google Scholar 

  41. He P, Zhou G, Zhang M et al (2023) Improving temporal knowledge graph embedding using tensor factorization. Applied Intell 53(8):8746–876. https://doi.org/10.1007/s10489-021-03149-w

    Article  MATH  Google Scholar 

  42. Zhao X, Li A, Jiang R et al (2023) Householder Transformation-Based Temporal Knowledge Graph Reasoning. Electron 12(9):200. https://doi.org/10.3390/electronics12092001

    Article  MATH  Google Scholar 

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Funding

This work is supported by the 111 Project (No. D23006), the National Natural Science Foundation of China (No. 62076045), and the Dalian Major Projects of Basic Research (No. 2023JJ11CG002).

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Conceptualization, Methodology, Software, Validation, Writing - original draft: Shuo Wang. Data curation, Visualization, Formal Analysis: Shuxu Chen. Resources, Funding acquisition, Supervision, Writing - review and editing: Zhaoqian Zhong.

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Correspondence to Zhaoqian Zhong.

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Wang, S., Chen, S. & Zhong, Z. Global and local information-aware relational graph convolutional network for temporal knowledge graph completion. Appl Intell 55, 125 (2025). https://doi.org/10.1007/s10489-024-05987-w

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