Abstract
Link prediction tackles the prediction of missing facts in an incomplete knowledge graph (KG) and has been widely explored in reasoning and information retrieval. The vast majority of existing methods perform link prediction on static KGs, with the assumption that the relational facts are generally correct. However, some facts may not be universally valid, as they tend to evolve. Despite the prevalence of temporal knowledge graphs (TKGs) with evolving facts, the studies on such data for temporal link prediction are still far from resolved. In this paper, we propose SiepNet, a novel graph neural network for temporal link prediction, driven by local Structural Information and Evolutionary Patterns. Specifically, SiepNet captures the local structural information based on a relation-aware GNN architecture, and incorporates temporal attention to model long- and short-range historical dependencies hidden in TKGs. Moreover, SiepNet integrates local structures and evolutionary patterns to enhance the semantic representation of evolving facts in TKGs. The extensive experiments on five real-world TKG datasets demonstrate the effectiveness of our approach SiepNet in temporal link prediction, compared with the state-of-the-art methods.
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References
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Neural Information Processing Systems (NIPS), Lake Tahoe, America, pp. 1–9. ACM (2013)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Gated feedback recurrent neural networks. In: International Conference on Machine Learning, pp. 2067–2075. PMLR (2015)
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, Brussels, Belgium, pp. 2001–2011. ACL (2018)
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2d knowledge graph embeddings. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence, New Orleans, USA, pp. 1811–1818. AAAI (2018)
García-Durán, A., Dumancic, S., Niepert, M.: Learning sequence encoders for temporal knowledge graph completion. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, pp. 4816–4821. ACL (2018)
Han, Z., Chen, P., Ma, Y., Tresp, V.: Explainable subgraph reasoning for forecasting on temporal knowledge graphs. In: International Conference on Learning Representations (ICLR) (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, pp. 8352–8364 (2021)
He, Y., Zhang, P., Liu, L., Liang, Q., Zhang, W., Zhang, C.: HIP network: historical information passing network for extrapolation reasoning on temporal knowledge graph. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, (IJCAI), pp. 1915–1921 (2021)
Hinton, G., NitishSrivastava, A., Salakhutdinov, I.R.R.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci. 3(4), 212–223 (2012)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Hu, W., Yang, Y., Cheng, Z., Yang, C., Ren, X.: Time-series event prediction with evolutionary state graph. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 580–588 (2021)
Hui, B., Zhang, L., Zhou, X., Wen, X., Nian, Y.: Personalized recommendation system based on knowledge embedding and historical behavior. Appl. Intell. 52(1), 954–966 (2022)
Jiang, T., et al.: Towards time-aware knowledge graph completion. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan, pp. 1715–1724. ACL (2016)
Jin, W., Qu, M., Jin, X., Ren, X.: Recurrent event network: autoregressive structure inferenceover temporal knowledge graphs. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6669–6683. ACL, Virtual (2020)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (Poster), San Diego, California, USA. Openview (2015)
Lautenschlager, J., Shellman, S., Ward, M.: ICEWS event aggregations. Harvard Dataverse 3 (2015)
Leblay, J., Chekol, M.W.: Deriving validity time in knowledge graph. In: Companion Proceedings of the the Web Conference 2018, Lyon, France, pp. 1771–1776. ACM (2018)
Leetaru, K., Schrodt, P.A.: GDELT: global data on events, location, and tone, 1979–2012. In: International Studies Association, San Francisco, California, USA, pp. 1–49 (2013)
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 (Volume 1: Long Papers), pp. 4732–4743, Berkeley Hotel, Bangkok, Thailand. ACL (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. ACM, Virtual (2021)
Liu, Y., Ma, Y., Hildebrandt, M., Joblin, M., Tresp, V.: TLogic: temporal logical rules for explainable link forecasting on temporal knowledge graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 4120–4127 (2022)
Park, N., Liu, F., Mehta, P., Cristofor, D., Faloutsos, C., Dong, Y.: EvoKG: jointly modeling event time and network structure for reasoning over temporal knowledge graphs. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp. 794–803 (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
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, Banff, Alberta, Canada, pp. 697–706. ACM (2007)
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, Barceló Bávaro Convention Centre, Punta Cana, Dominican Republic, pp. 8306–8319. ACL (2021)
Sun, Z., Deng, Z.H., Nie, J.Y., Tang, J.: RotatE: knowledge graph embedding by relational rotation in complex space. In: International Conference on Learning Representations, Vancouver, Canada. OpenReview (2018)
Trivedi, R., Dai, H., Wang, Y., Song, L.: Know-evolve: deep temporal reasoning for dynamic knowledge graphs. In: International Conference on Machine Learning, Sydney, Australia, pp. 3462–3471. PMLR (2017)
Trivedi, R., Farajtabar, M., Biswal, P., Zha, H.: DyRep: learning representations over dynamic graphs. In: International Conference on Learning Representations, New Orleans, Louisiana, USA. OpenReview (2019)
Ward, M.D., Beger, A., Cutler, J., Dickenson, M., Dorff, C., Radford, B.: Comparing GDELT and ICEWS event data. Analysis 21(1), 267–297 (2013)
Wu, J., Cao, M., Cheung, J.C.K., Hamilton, W.L.: 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 (2020)
Xiao, H., Chen, Y., Shi, X.: Knowledge graph embedding based on multi-view clustering framework. IEEE Trans. Knowl. Data Eng. 33(2), 585–596 (2019)
Yang, B., Yih, S.W.t., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the International Conference on Learning Representations (ICLR) 2015, San Diego, California, USA. OpenReview (2015)
Xu, Y., Ou, J., Xu, H., Fu, L.: Temporal knowledge graph reasoning with historical contrastive learning. CoRR (2022)
Zhu, C., Chen, M., Fan, C., Cheng, G., Zhang, Y.: Learning from history: modeling temporal knowledge graphs with sequential copy-generation networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4732–4740. AAAI, Virtual (2021)
Acknowledgment
This work was supported by the National Natural Science Foundation of China (No. U2003208 and No. 62172451), the Scientific and Technological Innovation 2030-Major software of New Generation Artificial Intelligence (No. 2020AAA0109601), and the Open Research software of Zhejiang Lab (No. 2022KG0AB01).
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Chen, T., Long, J., Yang, L., Li, G., Luo, S., Xiao, M. (2023). Joint Embedding of Local Structures and Evolutionary Patterns for Temporal Link Prediction. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14177. Springer, Cham. https://doi.org/10.1007/978-3-031-46664-9_8
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