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Graph Semantics Based Neighboring Attentional Entity Alignment for Knowledge Graphs

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Intelligent Computing Theories and Application (ICIC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12837))

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Abstract

Entity alignment is the task of matching entities in different knowledge graphs if they refer to the same real-world identity. A promising method for entity alignment is to use embedding methods to learn knowledge graph representations and align entities by measuring their embedding distance. However, when dealing with the challenge of structural heterogeneity between knowledge graphs, most existing entity alignment methods ignored the potential evidence provided by entity and relation semantics. In this paper, an entity alignment framework that incorporates graph semantic information with neighboring attention is proposed. The framework leverages both entity and relation semantic information by introducing the attention mechanism into a graph convolutional network module. In particular, an attention mechanism about neighboring relation semantic information is developed in the proposed framework to learn entity representations as well as to ignore unimportant neighborhoods. The experimental results on the real-world dataset WK3L demonstrates that the proposed framework consistently outperforms other state-of-the-art models.

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Acknowledgement

This work is supported by the National Key Research and Development Program of China No.2019YFC1709202 and 2019YFC1709200.

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Authors and Affiliations

  1. Beijing Laboratory of Advanced Information Networks, and Beijing Key Laboratory of Network System Architecture and Convergence, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Hanchen Wang, Jianfeng Li & Tao Luo

Authors
  1. Hanchen Wang
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  2. Jianfeng Li
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  3. Tao Luo
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Corresponding author

Correspondence to Hanchen Wang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Shenzhen University, Shenzhen, China

    Jianqiang Li

  4. Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia

    Valeriya Gribova

  5. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Wang, H., Li, J., Luo, T. (2021). Graph Semantics Based Neighboring Attentional Entity Alignment for Knowledge Graphs. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12837. Springer, Cham. https://doi.org/10.1007/978-3-030-84529-2_30

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  • DOI: https://doi.org/10.1007/978-3-030-84529-2_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-84528-5

  • Online ISBN: 978-3-030-84529-2

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