Abstract
The task of entity alignment between knowledge graphs (KGs) aims to find entities in two knowledge graphs that represent the same real-world entity. Recently, embedding-based entity alignment methods get extended attention. Most of them firstly embed the entities in low dimensional vectors space via relation structure, and then align entities via these learned embeddings combined with some entity similarity function. Even achieved promising performances, these methods are inadequate in utilizing entity type information. In this paper, we propose a novel entity alignment framework, which integrates entity embeddings and entity type information to achieve entity alignment. This framework uses encoding functions to extract the type features of entities for type matching, and combines the similarity of entity embeddings to improve the accuracy of entity alignment. Our experimental results on several real-world datasets shows that our proposed method achieves improvements on entity alignment compared with most methods, and is close to the state-of-the-art method on several metrics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Asamoah, C., Tao, L., Gai, K., Jiang, N.: Powering filtration process of cyber security ecosystem using knowledge graph. In: CSCloud, pp. 240–246 (2016)
Bordes, A., Usunier, N., GarcÃa-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, pp. 2787–2795 (2013)
Chen, M., Tian, Y.N., Chang, K., Skiena, S., Zaniolo, C.: Co-training embeddings of knowledge graphs and entity descriptions for cross-lingual entity alignment. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, pp. 3998–4004 (2018)
Chen, M., Tian, Y., Yang, M., Zaniolo, C.: Multilingual knowledge graph embeddings for cross-lingual knowledge alignment. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 1511–1517 (2017)
Cao, Y., Liu, Z., Li, C., Liu, Z., Li, J., Chua, T.: Multi-channel graph neural network for entity alignment. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, pp. 1452–1461 (2019)
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D Knowledge Graph Embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1811–1818 (2018)
Hao, Y., Zhang, Y., He, S., Liu, K. Zhao, J.: A joint embedding method for entity alignment of knowledge bases. In: China Conference on Knowledge Graph and Semantic Computing, pp. 3–14 (2016)
Isele, R., Bizer, C.: Active learning of expressive linkage rules using genetic programming. J. Web Semant. 23, 2–15 (2013)
Ji, G., He, S., Xu, L., Liu, K., Zhao. J.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 687–696 (2015)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, pp. 2181–2187 (2015)
Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., et al.: DBpedia – a large-scale, multilingual knowledge base extracted from wikipedia. Semant. Web J. 6(2), 167–195 (2015)
Nickel, M., Rosasco, L., Poggio, T.: Holographic embeddings of knowledge graphs. In: AAAI, pp. 1955–1961 (2016)
Nathani, D., Chauhan, J., Sharma, C., Kaul, M.: Learning attention-based embeddings for relation prediction in knowledge graphs. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, pp. 4710–4723 (2019)
Sun, Z., Hu, W., Zhang, Q., Qu, Y.: Bootstrapping entity alignment with knowledge graph embedding. In: IJCAI, pp. 4396–4402 (2018)
Sun, Z., Hu, W., Li, C.: Cross-lingual entity alignment via joint attribute-preserving embedding. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10587, pp. 628–644. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68288-4_37
Schlichtkrull, M., Kipf, T.N., Bloem, P., Berg, R.V., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: European semantic web conference, pp. 593–607 (2018)
Shi, B., Weninger, T.: Proje: Embedding projection for knowledge graph completion. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 1236–1242 (2017)
Trsedya, B.D., Qi, J., Rui, Z.: Entity alignment between knowledge graphs using attribute embeddings. In: AAAI, pp. 297–304 (2019)
Trouillon, T., Welbl, J., Riedel, S., Gaussier, E., Bouchard, G.: Complex embeddings for simple link prediction. In: ICML, pp. 2071–2080 (2016)
Vrandecic, D., Krotzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)
Wang, Z., Lv, Q., Lan, X., Zhang, Y.: Cross-lingual knowledge graph alignment via graph convolutional networks. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 349–357 (2018)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp. 1112–1119 (2014)
Xiong, W., Hoang, T., Wang, W.Y.: Deeppath: A reinforcement learning method for knowledge graph reasoning. arXiv preprint arXiv:1707.06690 (2017)
Yih, W.T., Chang, M.W., He, X., Gao J.: Semantic parsing via staged query graph generation: question answering with knowledge base. In: Proceedings of Association for Computational Linguistics, pp. 1321–1331 (2015)
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)
Zhang, Q., Sun, Z., Hu, W., Chen, M., Guo, L., Qu, Y.: Multi-view knowledge graph embedding for entity alignment. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pp. 5429–5435 (2019)
Zhu, H., Xie, R., Liu, Z., Sun, M.: Iterative entity alignment via joint knowledge embeddings. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, pp. 4258–4264 (2017)
Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma W.Y.: Collaborative knowledge base embedding for recommender systems. In: Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 353–362 (2016)
Acknowledgment
. The work was supported by the National Natural Science Foundation of China (61402087), the Natural Science Foundation of Hebei Province (F2019501030), the Natural Science Foundation of Liaoning Province (2019-MS-130), the Key Project of Scientific Research Funds in Colleges and Universities of Hebei Education Department (ZD2020402), the Fundamental Research Funds for the Central Universities (N2023019), and in part by the Program for 333 Talents in Hebei Province (A202001066).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Song, X., Zhang, H., Bai, L. (2021). Entity Alignment Between Knowledge Graphs Using Entity Type Matching. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12815. Springer, Cham. https://doi.org/10.1007/978-3-030-82136-4_47
Download citation
DOI: https://doi.org/10.1007/978-3-030-82136-4_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82135-7
Online ISBN: 978-3-030-82136-4
eBook Packages: Computer ScienceComputer Science (R0)