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

05/11/2023
by   Jianheng Tang, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/10/2022

An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection

Knowledge graph integration typically suffers from the widely existing d...
research
05/12/2020

Neighborhood Matching Network for Entity Alignment

Structural heterogeneity between knowledge graphs is an outstanding chal...
research
10/30/2020

A Critical Assessment of State-of-the-Art in Entity Alignment

In this work, we perform an extensive investigation of two state-of-the-...
research
01/26/2021

Towards Entity Alignment in the Open World: An Unsupervised Approach

Entity alignment (EA) aims to discover the equivalent entities in differ...
research
03/02/2022

SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs

Entity alignment, aiming to identify equivalent entities across differen...
research
05/12/2021

Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding

Knowledge Graph (KG) alignment is to discover the mappings (i.e., equiva...
research
01/14/2022

Training Free Graph Neural Networks for Graph Matching

We present TFGM (Training Free Graph Matching), a framework to boost the...

Please sign up or login with your details

Forgot password? Click here to reset