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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.


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

Show Author's information Jianliang Gao( )Xiangyue LiuYibo ChenFan Xiong
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

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.

Keywords:

knowledge graph, entity alignment, graph convolutional network
Received: 15 May 2021 Revised: 15 July 2021 Accepted: 30 July 2021 Published: 09 December 2021 Issue date: August 2022
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Publication history

Received: 15 May 2021
Revised: 15 July 2021
Accepted: 30 July 2021
Published: 09 December 2021
Issue date: August 2022

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© The author(s) 2022

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61873288), Research on Key Technologies and Application for the Time Series Data of State Grid Hunan Electirc Power Company (No. 5216A00036), the Hunan Key Laboratory for Internet of Things in Electricity (No. 2019TP1016), and CAAI-Huawei MindSpore Open Fund.

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