Informed Multi-context Entity Alignment

TL;DR

This paper introduces IMEA, a novel entity alignment model that leverages Transformer-based multi-context features and holistic reasoning to improve knowledge graph integration accuracy.

Contribution

The paper proposes a Transformer-based model with holistic reasoning and soft label editing for multi-context entity alignment, addressing limitations of previous methods.

Findings

Outperforms existing state-of-the-art methods on benchmark datasets.

Effectively captures relation, path, and neighborhood contexts.

Improves alignment accuracy through holistic reasoning and soft label editing.

Abstract

Entity alignment is a crucial step in integrating knowledge graphs (KGs) from multiple sources. Previous attempts at entity alignment have explored different KG structures, such as neighborhood-based and path-based contexts, to learn entity embeddings, but they are limited in capturing the multi-context features. Moreover, most approaches directly utilize the embedding similarity to determine entity alignment without considering the global interaction among entities and relations. In this work, we propose an Informed Multi-context Entity Alignment (IMEA) model to address these issues. In particular, we introduce Transformer to flexibly capture the relation, path, and neighborhood contexts, and design holistic reasoning to estimate alignment probabilities based on both embedding similarity and the relation/entity functionality. The alignment evidence obtained from holistic reasoning is further injected back into the Transformer via the proposed soft label editing to inform embedding learning. Experimental results on several benchmark datasets demonstrate the superiority of our IMEA model compared with existing state-of-the-art entity alignment methods.