Abstract
Entity prediction and relation prediction are the two major tasks of temporal knowledge graph (TKG) reasoning. The key to answering queries about future events is to understand historical trends and extract the information most likely to affect the future, i.e., the TKG reasoning task is both influenced by the trends of time-evolving graphs and directly driven by the facts relevant to a specific query. Existing methods mostly build models separately for these two characteristics, namely evolution representation learning and query-specific methods, failing to integrate these two crucial factors that determine reasoning results into a single framework. In this paper, we propose a novel temporal hybrid reasoning network (tHR-NET), simultaneously considering the modeling of graph feature space evolution and the enhancement of query-related feature representations in TKG. Specifically, we introduce a global graph space evolution module to extract graph trends, which influence entity/relation representations at each timestamp through a temporal view projection. Additionally, we propose a query-specific increment module for targeted enhancement of entity and relation representations, capturing query-related factors over extended durations. Through extensive experiments on real datasets, tHR-NET demonstrates distinct advantages in parallel entity and relation prediction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barros, C.D.T., Mendonça, M.R.F., Vieira, A.B., Ziviani, A.: A survey on embedding dynamic graphs. ACM Comput. Surv. 55(1), 1–37 (2023)
Chen, D., O’Bray, L., Borgwardt, K.: Structure-aware transformer for graph representation learning. In: Proceedings of the 39th International Conference on Machine Learning, vol. 162, pp. 3469–3489 (2022)
Chen, J., Wang, X., Xu, X.: GC-LSTM: graph convolution embedded LSTM for dynamic network link prediction. Appl. Intell. 52(7), 7513–7528 (2022)
Dasgupta, S.S., Ray, S.N., Talukdar, P.: HyTE: hyperplane-based Temporally aware knowledge graph embedding. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2001–2011 (2018)
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018)
Gao, Y., Feng, L., Kan, Z., Han, Y., Qiao, L., Li, D.: Modeling precursors for temporal knowledge graph reasoning via auto-encoder structure. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, pp. 2044–2051 (2022)
Garcia-Duran, A., Dumančić, S., Niepert, M.: Learning sequence encoders for temporal knowledge graph completion. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 4816–4821 (2018)
Goyal, P., Kamra, N., He, X., Liu, Y.: Dyngem: deep embedding method for dynamic graphs (2018)
Han, Z., Chen, P., Ma, Y., Tresp, V.: Explainable subgraph reasoning for forecasting on temporal knowledge graphs. In: International Conference on Learning Representations (2021)
Han, Z., Ding, Z., Ma, Y., Gu, Y., Tresp, V.: 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 (2021)
Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. In: International Conference on Learning Representations (2017)
Jin, W., et al.: Recurrent event network: global structure inference over temporal knowledge graph (2019)
Leblay, J., Chekol, M.W.: Deriving validity time in knowledge graph. In: Companion Proceedings of the the Web Conference 2018, pp. 1771–1776 (2018)
Leetaru, K., Schrodt, P.A.: Gdelt: global data on events, location, and tone. ISA Annual Convention (2013)
Li, Z., et al.: Complex evolutional pattern learning for temporal knowledge graph reasoning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, pp. 290–296 (2022)
Li, Z., et al.: HiSMatch: historical structure matching based temporal knowledge graph reasoning. In: Findings of the Association for Computational Linguistics: EMNLP 2022, pp. 7328–7338 (2022)
Li, Z., et al.: Search from history and reason for future: two-stage reasoning on temporal knowledge graphs. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (2021)
Li, Z., et al.: 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, pp. 408–417 (2021)
Liu, D., et al.: User-event graph embedding learning for context-aware recommendation. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1051–1059 (2022)
Mahdisoltani, F., Biega, J.A., Suchanek, F.M.: Yago3: a knowledge base from multilingual Wikipedias. In: Conference on Innovative Data Systems Research (2015)
Mao, H., Schwarzkopf, M., Venkatakrishnan, S.B., Meng, Z., Alizadeh, M.: Learning scheduling algorithms for data processing clusters. In: Proceedings of the ACM Special Interest Group on Data Communication, pp. 270–288. Beijing China (2019)
Miao, S., Liu, M., Li, P.: Interpretable and generalizable graph learning via stochastic attention mechanism. In: Proceedings of the 39th International Conference on Machine Learning, vol. 162, pp. 15524–15543 (2022)
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38
Shang, C., Tang, Y., Huang, J., Bi, J., He, X., Zhou, B.: End-to-end structure-aware convolutional networks for knowledge base completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 3060–3067 (2019)
Sun, H., Zhong, J., Ma, Y., Han, Z., He, K.: TimeTraveler: reinforcement learning for temporal knowledge graph forecasting. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
Trouillon, T., Welbl, J., Riedel, S., Gaussier, E., Bouchard, G.: Complex embeddings for simple link prediction. In: Proceedings of The 33rd International Conference on Machine Learning, pp. 2071–2080 (2016)
Wang, S., Cai, X., Zhang, Y., Yuan, X.: CRnet: modeling concurrent events over temporal knowledge graph. In: Sattler, U., et al. (eds.) ISWC 2022. LNCS, vol. 13489, pp. 516–533. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19433-7_30
Yang, B., Yih, W.T., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases (2014)
Zhang, M., Xia, Y., Liu, Q., Wu, S., Wang, L.: Learning latent relations for temporal knowledge graph reasoning. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, pp. 12617–12631 (2023)
Zheng, C., Fan, X., Wang, C., Qi, J.: Gman: a graph multi-attention network for traffic prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 1234–1241 (2020)
Zhu, C., Chen, M., Fan, C., Cheng, G.: Learning from history: modeling temporal knowledge graphs with sequential copy-generation networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 5, pp. 4732–4740 (2021)
Acknowledgements
The authors would like to thank the anonymous reviewers for their valuable comments. This work is supported by National Key Research and Development Program of China No. 2022-JCJQ-JJ-0587.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhao, Y., Liu, Y., Wan, Z., Wang, H. (2024). tHR-Net: A Hybrid Reasoning Framework for Temporal Knowledge Graph. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 562. Springer, Cham. https://doi.org/10.1007/978-3-031-54528-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-54528-3_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-54527-6
Online ISBN: 978-3-031-54528-3
eBook Packages: Computer ScienceComputer Science (R0)