Multilingual entity alignment by abductive knowledge reasoning on multiple knowledge graphs

Abstract

Objectives:

Entity alignment (EA) seeks to identify similar real-world objects in different multilingual knowledge graphs (KGs), also known as ontology alignment. EA assists in handling a wide range of language semantics and in building integrated knowledge bases. However, most mainstream studies have focused on structural information, paying little attention to insufficient contextual information and limited handling of complex relationships. This paper aims to address these limitations and improve EA performance and efficiency.

Methods:

This paper investigates multilingual EA techniques and proposes a novel Abductive Knowledge Reasoning (AKR) model to address these issues. AKR can compute complex relationship semantics context by reasoning and enrich counterpart entity contextual information through centrality calculation, which helps connect distant entities in multilingual KGs.

Novelty:

The proposed AKR model introduces a new approach to EA by integrating centrality calculation and relational semantics reasoning. This method overcomes the limitations of existing EA techniques by effectively handling insufficient contextual information and complex relationships in multilingual KGs.

Findings:

AKR outperforms all state-of-the-art EA models across five datasets. AKR achieves $Hit@1$ score of 79.4%, for entity alignment between Chinese-to-English knowledge graphs representing 19.9% improvement over the best-performing translation-based model, Neighborhood-Aware Attentional Representation Entity Alignment, and a 5.0% improvement over the best-performing graph neural network-based model, Relational Semantics Augmentation.