A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment
This addresses the challenge of aligning entities across knowledge graphs without supervision, which is incremental as it builds on existing embedding-based methods by better utilizing structural information.
The paper tackles the problem of entity alignment across knowledge graphs by introducing an unsupervised framework called FGWEA, which uses the Fused Gromov-Wasserstein distance to compare entity semantics and structures, and it outperforms 21 baselines including supervised methods on datasets covering 14 KGs in five languages.
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.