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