Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs

Abstract

Entity Alignment (EA) in Knowledge Graphs (KGs) is a crucial task for the integration of multiple KGs, facilitating the amalgamation of multi-source knowledge and enhancing support for downstream applications. In recent years, unsupervised EA methods have demonstrated remarkable efficacy in leveraging graph structures or utilizing auxiliary information. However, the increasing complexity of many modeling methods limits their applicability to large KGs in real-world scenarios. Given that most EA encoders primarily focus on modeling one-hop neighborhoods within the KG’s graph structure while neglecting similarities among multi-hop neighborhoods, we propose an efficient and effective unsupervised EA method, MPGT-Align, based on a multi-hop pruning graph transformer. The core innovation of MPGT-Align lies in mining multi-hop neighborhood features of entities through two components: Pruning-hop2Token and Attention-based Transformer encoder. The former aggregates only those multi-hop neighborhoods that contribute to alignment targets, inspired by search pruning algorithms. The latter empowers MPGT-Align to adaptively extract more effective alignment information from both entity itself and its multi-hop neighbors. Furthermore, Pruning-hop2Token serves as a non-parametric method that not only reduces model parameters, but also allows MPGT-Align to be trained with small batch sizes, thereby enabling efficient handling of large KGs. Extensive experiments conducted across various benchmark datasets demonstrate that our method consistently outperforms most existing supervised and unsupervised EA techniques.