Embedding-based entity alignment between multi-source temporal knowledge graphs

Abstract

The goal of entity alignment is to identify entities in two multi-source knowledge graphs (KGs) that represent the same real-world object. Recent researches on multi-source entity alignment mainly concentrate on static KGs. In fact, temporal KGs have become valuable resources for numerous artificial intelligence applications, and entity alignment between multi-source temporal KGs is becoming more and more important. Current entity alignment models cannot support temporal tasks and fail to deal with the attributes with low literal similarity that share the same semantics through attribute embedding. Therefore, we propose a RDF (Resource Description Framework)-based model for representing temporal KGs, and an embedding-based entity alignment method for multi-source temporal KGs. This method computes for the similarity of temporal information and generates aligned attribute pairs in the predicate alignment module. We design an interactive module to make matched attributes and the matched entities help to find each other based on aligned attribute pairs. This module can calculate the similarity of attributes with low literal similarity. After getting the structure similarity of the structure embedding module, the final entity alignment result of temporal KGs is produced by the calculation of a binary linear regression function. Experimental results demonstrate that our proposed model outperforms existing approaches significantly.