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
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.findings-acl.553.pdf