Temporal knowledge completion enhanced self-supervised entity alignment

Abstract

Temporal graph entity alignment aims at finding the equivalent entity pairs across different temporal knowledge graphs (TKGs). Primarily methods mainly utilize a time-aware and relationship-aware approach to embed and align. However, the existence of long-tail entities in TKGs still restricts the accuracy of alignment, as the limited neighborhood information may restrict the available neighborhood information for obtaining high-quality embeddings, and hence would impact the efficiency of entity alignment in representation space. Moreover, most previous researches are supervised, with heavy dependence on seed labels for alignment, restricting their applicability in scenarios with limited resources. To tackle these challenges, we propose a Temporal Knowledge Completion enhanced Self-supervised Entity Alignment (TSEA). We argue that, with high-quality embeddings, the entities would be aligned in a self-supervised manner. To this end, TSEA is constituted of two modules: A graph completion module to predict the missing links for the long-tailed entities. With the improved graph, TSEA further incorporates a self-supervised entity alignment module to achieve unsupervised alignment. Experimental results on widely adopted benchmarks demonstrate improved performance compared to several recent baseline methods. Additional ablation experiments further corroborate the efficacy of the proposed modules.