Abstract
As a fundamental task of knowledge graph integration, entity alignment (EA) matches equivalent entities across knowledge graphs (KGs). Temporal knowledge graphs (TKGs) enhance static KGs with temporal information. Traditional EA approaches tackle alignments of static KGs, but cannot effectively deal with TKGs. Therefore, temporal EA solutions are called for. To this end, we propose a time-aware graph attention network for entity alignment (TGA-EA). Generally, we learn high-quality temporal-relational entity embeddings for alignments by systematically integrating temporal information into KG embeddings. We propose three temporal modeling methods to effectively represent and integrate temporal information into both entities and relations. Then we construct temporal enhanced graph attention to produce target temporal-relational entity embeddings with temporal entities and temporal relations. Thanks to the powerful design, TGA-EA achieves promising performances with sparse alignment seeds. Extensive experiments on five datasets demonstrate our approach’s obvious advantages over previous works.
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References
Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: A survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)
Sun, Z., et al.: A benchmarking study of embedding-based entity alignment for knowledge graphs. arXiv preprint arXiv:2003.07743 (2020)
Chen, M., Tian, Y., Yang, M., Zaniolo, C.: Multilingual knowledge graph embeddings for cross-lingual knowledge alignment. In: IJCAI, pp. 1511–1517 (2017)
Sun, Z., Hu, W., Zhang, Q., Qu, Y.: Bootstrapping entity alignment with knowledge graph embedding. In: IJCAI, pp. 4396–4402 (2018)
Zhu, H., Xie, R., Liu, Z., Sun, M.: Iterative entity alignment via knowledge embeddings. In: IJCAI, pp. 4258–4264 (2017)
Mao, X., Wang, W., Xu, H., Lan, M., Wu, Y.: MRAEA: an efficient and robust entity alignment approach for cross-lingual knowledge graph. In: WSDM, pp. 420–428 (2020)
Mao, X., Wang, W., Xu, H., Wu, Y., Lan, M.: Relational reflection entity alignment. In: CIKM, pp. 1095–1104 (2020)
Mao, X., Wang, W., Wu, Y., Lan, M.: Boosting the speed of entity alignment 10×: Dual attention matching network with normalized hard sample mining. In: WWW, pp. 821–832 (2021)
Wang, Z., Lv, Q., Lan, X., Zhang, Y.: Cross-lingual knowledge graph alignment via graph convolutional networks. In: EMNLP, pp. 349–357 (2018)
Cai, B., Xiang, Y., Gao, L., Zhang, H., Li, Y., Li, J.: Temporal knowledge graph completion: A survey. arXiv preprint arXiv:2201.08236 (2022)
Dasgupta, S.S., Ray, S.N., Talukdar, P.P.: HyTE: hyperplane-based temporally aware knowledge graph embedding. In: EMNLP, pp. 2001–2011 (2018)
Sadeghian, A., Armandpour, M., Colas, A., Wang, D.Z.: ChronoR: rotation based temporal knowledge graph embedding. In: AAAI, pp. 6471–6479 (2021)
Xu, C., Su, F., Lehmann, J.: Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphs. In: EMNLP, pp. 8999–9010 (2021)
Xu, C., Su, F., Xiong, B., Lehmann, J.: Time-aware Entity Alignment using Temporal Relational Attention. In: WWW, pp. 788–797 (2022)
Song, X., Bai, L., Liu, R., Zhang, H.: Temporal Knowledge Graph Entity Alignment via Representation Learning. In: DASFAA, pp. 391–406 (2022)
Vaswani, A., et al.: Attention is All you Need. arXiv preprint arXiv:1706.03762 (2017)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (Grant Nos. 62002262, 62172082, 62072086, 62072084).
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Sun, C., Jin, Y., Shen, D., Nie, T., Wang, X., Xiao, Y. (2023). Enhancing Knowledge Graph Attention by Temporal Modeling for Entity Alignment with Sparse Seeds. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13944. Springer, Cham. https://doi.org/10.1007/978-3-031-30672-3_43
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DOI: https://doi.org/10.1007/978-3-031-30672-3_43
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