Abstract
Entity alignment is the task of identifying corresponding entities across different knowledge graphs (KGs). Although recent embedding-based entity alignment methods have shown significant advancements, they still struggle to fully utilize KG structural information. In this paper, we introduce FGWEA, an unsupervised entity alignment framework that leverages the Fused Gromov-Wasserstein (FGW) distance, allowing for a comprehensive comparison of entity semantics and KG structures within a joint optimization framework. To address the computational challenges associated with optimizing FGW, we devise a three-stage progressive optimization algorithm. It starts with a basic semantic embedding matching, proceeds to approximate cross-KG structural and relational similarity matching based on iterative updates of high-confidence entity links, and ultimately culminates in a global structural comparison between KGs. We perform extensive experiments on four entity alignment datasets covering 14 distinct KGs across five languages. Without any supervision or hyper-parameter tuning, FGWEA surpasses 21 competitive baselines, including cutting-edge supervised entity alignment methods. Our code is available at https://github.com/squareRoot3/FusedGW-Entity-Alignment.- Anthology ID:
- 2023.findings-acl.205
- 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:
- 3320–3334
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.205
- DOI:
- 10.18653/v1/2023.findings-acl.205
- Cite (ACL):
- Jianheng Tang, Kangfei Zhao, and Jia Li. 2023. A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment. In Findings of the Association for Computational Linguistics: ACL 2023, pages 3320–3334, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment (Tang et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.findings-acl.205.pdf