Abstract
Temporal knowledge graphs store a large number of temporal facts that simulate the dynamic interactions of entities along the timeline. Since existing temporal knowledge graphs often suffer from incompleteness, it is crucial to build time-aware representation learning models that help to infer the missing temporal facts. However, most of the existing models for temporal knowledge graph reasoning focus on mining temporal associations between entities, and do not fully exploit spatial information contained in entities. To this end, we propose spatial-temporal network(ST-Net), a new representation learning model for temporal knowledge graphs, which has both temporal and spatial awareness capabilities. Specifically, ST-Net enriches the hidden features of entities by simultaneously fusing their temporal and spatial information. At the same time, we introduce the core idea of Copy-Generation Networks, which predicts future facts based on either the historical vocabulary or the whole entity vocabulary. We evaluate our proposed method via link prediction at future times on three benchmark datasets. Through extensive experiments, we demonstrate ST-Net has superior performance on the future link prediction tasks.
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References
Leblay, J., Chekol, M.W.: Deriving validity time in knowledge graph. In: Companion Proceedings of the The Web Conference 2018, pp. 1771–1776 (2018)
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)
Trivedi, R., Dai, H., Wang, Y., Song, L.: Know-evolve: deep temporal reasoning for dynamic knowledge graphs. In: International Conference on Machine Learning, pp. 3462–3471. PMLR (2017)
Goel, R., Kazemi, S.M., Brubaker, M., Poupart, P.: Diachronic embedding for temporal knowledge graph completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3988–3995 (2020)
Jin, W., Qu, M., Jin, X., Ren, X.: Recurrent event network: autoregressive structure inference over temporal knowledge graphs. arXiv preprint arXiv:1904.05530 (2019)
Zhu, C., Chen, M., Fan, C., Cheng, G., Zhan, Y.: Learning from history: modeling temporal knowledge graphs with sequential copy-generation networks. arXiv preprint arXiv:2012.08492 (2020)
Deng, S., Rangwala, H., Ning, Y.: Dynamic knowledge graph based multi-event forecasting. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1585–1595 (2020)
Xiao, H., Huang, M., Meng, L., Zhu, X.: SSP: semantic space projection for knowledge graph embedding with text descriptions. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
Chen, X., Chen, M., Shi, W., Sun, Y., Zaniolo, C.: Embedding uncertain knowledge graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3363–3370 (2019)
Ji, S., Pan, S., Cambria, E., Marttinen, P., Philip, S.Y.: A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans. Neural Netw. Learn. Syst. 33(2), 494–514 (2021)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, vol. 26 (2013)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Twenty-ninth AAAI Conference on Artificial Intelligence (2015)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: International Conference on Machine Learning, pp. 2071–2080. PMLR (2016)
Yang, B., Yih, W.T., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014)
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
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2d knowledge graph embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Guo, L., Sun, Z., Hu, W.: Learning to exploit long-term relational dependencies in knowledge graphs. In: International Conference on Machine Learning, pp. 2505–2514. PMLR (2019)
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)
Boschee, E., Lautenschlager, J., O’Brien, S., Shellman, S., Starz, J., Ward, M.: ICEWS coded event data. Harvard Dataverse, vol. 12 (2015)
García-Durán, A., Dumančić, S., Niepert, M.: Learning sequence encoders for temporal knowledge graph completion. arXiv preprint arXiv:1809.03202 (2018)
Sun, Z., Deng, Z.H., Nie, J.Y., Tang, J.: Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019)
Kazemi, S.M., Poole, D.: Simple embedding for link prediction in knowledge graphs. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
Xu, C., Nayyeri, M., Alkhoury, F., Yazdi, H.S., Lehmann, J.: Temporal knowledge graph embedding model based on additive time series decomposition. arXiv preprint arXiv:1911.07893 (2019)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256. JMLR Workshop and Conference Proceedings (2010)
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The research is supported by The Natural Science Foundation of Guangdong Province (No.2018A030313934).
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Zuo, Y., Zhou, Y., Liu, Z., Wu, J., Zhan, M. (2022). Learning Temporal and Spatial Embedding for Temporal Knowledge Graph Reasoning. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13630. Springer, Cham. https://doi.org/10.1007/978-3-031-20865-2_10
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