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Open Access

MHGCN: Multiview Highway Graph Convolutional Network for Cross-Lingual Entity Alignment

School of Computer Science and Engineering, Central South University, Changsha 410083, China
Information and Communication Branch, State Grid Hunan Electric Power Company Limited, Changsha 410004, China
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Abstract

Knowledge graphs (KGs) provide a wealth of prior knowledge for the research on social networks. Cross-lingual entity alignment aims at integrating complementary KGs from different languages and thus benefits various knowledge-driven social network studies. Recent entity alignment methods often take an embedding-based approach to model the entity and relation embedding of KGs. However, these studies mostly focus on the information of the entity itself and its structural features but ignore the influence of multiple types of data in KGs. In this paper, we propose a new embedding-based framework named multiview highway graph convolutional network (MHGCN), which considers the entity alignment from the views of entity semantic, relation semantic, and entity attribute. To learn the structural features of an entity, the MHGCN employs a highway graph convolutional network (GCN) for entity embedding in each view. In addition, the MHGCN weights and fuses the multiple views according to the importance of the embedding from each view to obtain a better entity embedding. The alignment entities are identified based on the similarity of entity embeddings. The experimental results show that the MHGCN consistently outperforms the state-of-the-art alignment methods. The research also will benefit knowledge fusion through cross-lingual KG entity alignment.

References

[1]
Z. Wang, T. Chen, J. Ren, W. Yu, H. Cheng, and L. Lin, Deep reasoning with knowledge graph for social relationship understanding, in Proc. of the 27th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 2018, pp. 1021-1028.
[2]
J. Qian, X. Y. Li, C. Zhang, L. Chen, T. Jung, and J. Han, Social network de-anonymization and privacy inference with knowledge graph model, IEEE Transactions on Dependable and Secure Computing, vol. 16, no. 4, pp. 679-692, 2019.
[3]
Z. Sun, J. Yang, J. Zhang, A. Bozzon, L. K. Huang, and C. Xu, Recurrent knowledge graph embedding for effective recommendation, in Proc. of the 12th ACM Conference on Recommender Systems, Vancouver British Columbia, Cananda, 2018, pp. 297-305.
[4]
C. Sarasua, E. Simperl, and N. F. Noy, Crowdmap: Crowdsourcing ontology alignment with microtasks, in Proc. of the 11th International Conference on the Semantic Web - Volume Part I, Boston, MA, USA, 2012, pp. 525-541.
[5]
X. Wang, K. Liu, S. He, S. Liu, Y. Zhang, and J. Zhao, Multi-source knowledge bases entity alignment by leveraging semantic tags, Chinese Journal of Computers, vol. 40, no. 3, pp. 701-711, 2017.
[6]
Q. Zhang, Z. Sun, W. Hu, M. Chen, L. Guo, and Y. Qu, Multi-view knowledge graph embedding for entity alignment, in Proc. of the 28th International Joint Conference on Artificial Intelligence, Macao, China, 2019, pp. 5429-5435.
[7]
K. Yang, J. Zhu, and X. Guo, POI neural-rec model via graph embedding representation, Tsinghua Science and Technology, vol. 26, no. 2, pp. 208-218, 2021.
[8]
A. Bordes, N. Usunier, A. Garcia-Durán, J. Weston, and O. Yakhnenko, Translating embeddings for modeling multi-relational data, in Proc. of the 26th International Conference on Neural Information Processing Systems - Volume 2, Lake Tahoe, NV, USA, 2013, pp. 2787-2795.
[9]
Z. Wang, J. Zhang, J. Feng, and Z. Chen, Knowledge graph embedding by translating on hyperplanes, in Proc. of the 28th AAAI Conference on Artificial Intelligence, Québec City, Canada, 2014, pp. 1112-1119.
[10]
Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu, Learning entity and relation embeddings for knowledge graph completion, in Proc. of the 29th AAAI Conference on Artificial Intelligence, Austin, TX, USA, 2015, pp. 2181-2187.
[11]
G. Ji, S. He, L. Xu, K. Liu, and J. Zhao, Knowledge graph embedding via dynamic mapping matrix, in Proc. of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China, 2015, pp. 687-696.
[12]
Y. Lin, Z. Liu, H. Luan, M. Sun, S. Rao, and S. Liu, Modeling relation paths for representation learning of knowledge bases, in Proc. of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, 2015, pp. 705-714.
[13]
C. Zhang, M. Zhou, X. Han, Z. Hu, and Y. Ji, Knowledge graph embedding for hyper-relational data, Tsinghua Science and Technology, vol. 22, no. 2, pp. 185-197, 2017.
[14]
T. N. Kipf and M. Welling, Semi-supervised classification with graph convolutional networks, in Proc. of the 5th International Conference on Learning Representations, Toulon, France, 2017, pp. 1-14.
[15]
D. Marcheggiani and I. Titov, Encoding sentences with graph convolutional networks for semantic role labeling, in Proc. of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 2017, pp. 1506-1515.
[16]
J. Zhang, X. Shi, S. Zhao, and I. King, Star-gcn: Stacked and reconstructed graph convolutional networks for recommender systems, in Proc. of the 28th International Joint Conference on Artificial Intelligence, Macao, China, 2019, pp. 4264-4270.
[17]
X. Zhao, Z. Wang, L. Gao, Y. Li, and S. Wang, Incremental face clustering with optimal summary learning via graph convolutional network, Tsinghua Science and Technology, vol. 26, no. 4, pp. 536-547, 2021.
[18]
Petar Veličković, G. Cucurull, A. Casanova, A. Romero, Pietro Liò, and Y. Bengio, Graph attention networks, in Proc. of the 6th International Conference on Learning Representations, Vancouver, Canada, 2018, pp. 1-12.
[19]
M. Chen, Y. Tian, M. Yang, and C. Zaniolo, Multilingual knowledge graph embeddings for cross-lingual knowledge alignment, in Proc. of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia, 2017, pp. 1511-1517.
[20]
Z. Wang, Q. Lv, X. Lan, and Y. Zhang, Cross-lingual knowledge graph alignment via graph convolutional networks, in Proc. of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018, pp. 349-357.
[21]
Y. Cao, Z. Liu, C. Li, J. Li, and T. S. Chua, Multi-channel graph neural network for entity alignment, in Proc. of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, pp. 1452-1461.
[22]
C. Li, Y. Cao, L. Hou, J. Shi, J. Li, and T. S. Chua, Semi-supervised entity alignment via joint knowledge embedding model and cross-graph model, in Proc. of the 2019 Conference on Empirical Methods in Natural Language Processing and the International Joint Conference on Natural Language Processing, Hong Kong, China, 2019, pp. 2723-2732.
[23]
R. K. Srivastava, K. Greff, and J. Schmidhuber, Highway networks, https://arxiv.org/abs/1505.00387v2, 2015.
[24]
Z. Sun, W. Hu, and C. Li, Cross-lingual entity alignment via joint attribute-preserving embedding, in Proc. of the 16th International Semantic Web Conference, Vienna, Austria, 2017, pp. 628-644.
[25]
Z. Sun, W. Hu, Q. Zhang, and Y. Qu, Bootstrapping entity alignment with knowledge graph embedding, in Proc. of the 27th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 2018, pp. 4396-4402.
[26]
Q. Zhu, X. Zhou, J. Wu, J. Tan, and L. Guo, Neighborhood-aware attentional representation for multilingual knowledge graphs, in Proc. of the 28th International Joint Conference on Artificial Intelligence, Macao, China, 2019, pp. 1943-1949.
[27]
X. Shi and Y. Xiao, Modeling multi-mapping relations for precise cross-lingual entity alignment, in Proc. of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China, 2019, pp. 813-822.
Tsinghua Science and Technology
Pages 719-728
Cite this article:
Gao J, Liu X, Chen Y, et al. MHGCN: Multiview Highway Graph Convolutional Network for Cross-Lingual Entity Alignment. Tsinghua Science and Technology, 2022, 27(4): 719-728. https://doi.org/10.26599/TST.2021.9010056
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Received: 15 May 2021
Revised: 15 July 2021
Accepted: 30 July 2021
Published: 09 December 2021
© The author(s) 2022

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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