A relation enhanced model for temporal knowledge graph alignment

Entity alignment (EA) aims to find entities that point to the same object in multiple knowledge graphs (KGs), i.e., finding equivalent entity pairs. In recent years, with the emergence of temporal knowledge graphs (TKGs), the TKG alignment has gained wide attention. Many existing methods embed temporal information into a vector space of low dimension and perform entity alignment by incorporating temporal embedding. However, most existing entity alignment methods ignore the relevance of relations in TKGs, which will make the entity alignment less accurate. To solve this problem, we propose a novel temporal knowledge graph alignment approach based on relation association and probabilistic calibration, namely, TKGA-RP. Specifically, to deal with the relevance between relations, we first construct the forward and reverse inverse relations with the beginning and the end of time. And then, we propose a novel way to capture the relational association (RA) of forward and inverse relations by considering the interacted entities. In addition, we constructed relational orthogonal matrices and temporal orthogonal matrices to capture the temporal awareness and relational awareness of the surrounding entities. The final entity representations are generated by graph neural networks. Finally, we calibrate the embedding distance between entities by probabilistic calibration (PC) to improve the performance. Experimental results on two TKG datasets show that our model achieves better performance than existing mainstream methods.