Entity alignment with fusing relation representation

Abstract

Entity alignment is the task of identifying entities from different knowledge graphs (KGs) that point to the same item and is important for KG fusion. In the real world, due to the heterogeneity between different KGs, equivalent entities often have different relations around them, so it is difficult for Graph Convolutional Network (GCN) to accurately learn the relation information in the KGs. Moreover, to solve the problem regarding inadequate utilisation of relation information in entity alignment, a novel GCN-based model, joint Unsupervised Relation Alignment for Entity Alignment (URAEA), is proposed. The model first employs a novel method for calculating relation embeddings by using entity embeddings, then constructs unsupervised seed relation alignments through these relation embeddings, and finally performs entity alignment together with relation alignment. In addition, the seed entity alignments are expanded based on the generated seed relation alignments. Experiments conducted on three real-world datasets show that this approach outperforms state-of-the-art methods.