A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment

Jianheng Tang, Kangfei Zhao, Jia Li*

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

7 引用 (Scopus)

摘要

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.

源语言英语
主期刊名Findings of the Association for Computational Linguistics, ACL 2023
出版商Association for Computational Linguistics (ACL)
3320-3334
页数15
ISBN(电子版)9781959429623
出版状态已出版 - 2023
活动61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 - Toronto, 加拿大
期限: 9 7月 202314 7月 2023

出版系列

姓名Proceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN(印刷版)0736-587X

会议

会议61st Annual Meeting of the Association for Computational Linguistics, ACL 2023
国家/地区加拿大
Toronto
时期9/07/2314/07/23

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