Time-aware structure matching for temporal knowledge graph alignment

Abstract

Entity alignment, aiming at identifying equivalent entity pairs across multiple knowledge graphs (KGs), serves as a vital step for knowledge fusion. As the majority of KGs undergo continuous evolution, existing solutions utilize graph neural networks (GNNs) to tackle entity alignment within temporal knowledge graphs (TKGs). However, this prevailing method often overlooks the consequential impact of relation embedding generation on entity embeddings through inherent structures. In this paper, we propose a novel model named Time-aware Structure Matching based on GNNs (TSM-GNN) that encompasses the learning of both topological and inherent structures. Our key innovation lies in a unique method for generating relation embeddings, which can enhance entity embeddings via inherent structure. Specifically, we utilize the translation property of knowledge graphs to obtain the entity embedding that is mapped into a time-aware vector space. Subsequently, we employ GNNs to learn global entity representation. To better capture the useful information from neighboring relations and entities, we introduce a time-aware attention mechanism that assigns different importance weights to different time-aware inherent structures. Experimental results on three real-world datasets demonstrate that TSM-GNN outperforms several state-of-the-art approaches for entity alignment between TKGs.