A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph
  Entity Alignment
Jianheng Tang Kangfei Zhao Jia Li
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.