Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs

Wu, Yuting and Liu, Xiao and Feng, Yansong and Wang, Zheng and Yan, Rui and Zhao, Dongyan (2019) Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs. In: The 28th International Joint Conference on Artificial Intelligence (IJCAI). IJCAI, pp. 5278-5284. ISBN 9780999241141

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Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods. Such approaches work by learning KG representations so that entity alignment can be performed by measuring the similarities between entity embeddings. While promising, prior works in the field often fail to properly capture complex relation information that commonly exists in multi-relational KGs, leaving much room for improvement. In this paper, we propose a novel Relation-aware Dual-Graph Convolutional Network (RDGCN) to incorporate relation information via attentive interactions between the knowledge graph and its dual relation counterpart, and further capture neighboring structures to learn better entity representations. Experiments on three real-world cross-lingual datasets show that our approach delivers better and more robust results over the state-of-the-art alignment methods by learning better KG representations.

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10 May 2019 12:40
Last Modified:
19 Sep 2023 03:31