A Novel Embedding Model for Knowledge Graph Entity Alignment Based on Graph Neural Networks

Abstract

The objective of the entity alignment (EA) task is to identify entities with identical semantics across distinct knowledge graphs (KGs) situated in the real world, which has garnered extensive recognition in both academic and industrial circles. Within this paper, a pioneering entity alignment framework named PCE-HGTRA is proposed. This framework integrates the relation and property information from varying KGs, along with the heterogeneity information present within the KGs. Firstly, by learning embeddings, this framework captures the similarity that exists between entities present across diverse KGs. Additionally, property triplets in KGs are used to generate property character-level embeddings, facilitating the transfer of entity embeddings from two distinct KGs onto an identical space. Secondly, the framework strengthens the property character-level embeddings using the transitivity rule to increase the count of entity properties. Then, in order to effectively capture the heterogeneous features in the entity neighborhoods, a heterogeneous graph transformer with relation awareness is designed to model the heterogeneous relations in KGs in the framework. Finally, comparative experimental results on four widely recognized real-world datasets demonstrate that PCE-HGTRA performs exceptionally well. In fact, its Hits@1 performance exceeds the best baseline by 7.94%, outperforming seven other state-of-the-art methods.