RHGN: Relation-gated Heterogeneous Graph Network for Entity Alignment in Knowledge Graphs

Xukai Liu, Kai Zhang, Ye Liu, Enhong Chen, Zhenya Huang, Linan Yue, Jiaxian Yan


Abstract
Entity Alignment, which aims to identify equivalent entities from various Knowledge Graphs (KGs), is a fundamental and crucial task in knowledge graph fusion. Existing methods typically use triple or neighbor information to represent entities, and then align those entities using similarity matching. Most of them, however, fail to account for the heterogeneity among KGs and the distinction between KG entities and relations. To better solve these problems, we propose a Relation-gated Heterogeneous Graph Network (RHGN) for entity alignment. Specifically, RHGN contains a relation-gated convolutional layer to distinguish relations and entities in the KG. In addition, RHGN adopts a cross-graph embedding exchange module and a soft relation alignment module to address the neighbor heterogeneity and relation heterogeneity between different KGs, respectively. Extensive experiments on four benchmark datasets demonstrate that RHGN is superior to existing state-of-the-art entity alignment methods.
Anthology ID:
2023.findings-acl.553
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8683–8696
Language:
URL:
https://aclanthology.org/2023.findings-acl.553
DOI:
10.18653/v1/2023.findings-acl.553
Bibkey:
Cite (ACL):
Xukai Liu, Kai Zhang, Ye Liu, Enhong Chen, Zhenya Huang, Linan Yue, and Jiaxian Yan. 2023. RHGN: Relation-gated Heterogeneous Graph Network for Entity Alignment in Knowledge Graphs. In Findings of the Association for Computational Linguistics: ACL 2023, pages 8683–8696, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
RHGN: Relation-gated Heterogeneous Graph Network for Entity Alignment in Knowledge Graphs (Liu et al., Findings 2023)
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