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Entity alignment (EA) aims to find entities in different knowledge graphs(KGs) that refer to the same object in the real world. Recent studiesincorporate temporal information to augment the representations of KGs. Theexisting methods for EA between temporal KGs (TKGs) utilize a time-awareattention mechanism to incorporate relational and temporal information intoentity embeddings. The approaches outperform the previous methods by usingtemporal information. However, we believe that it is not necessary to learn theembeddings of temporal information in KGs since most TKGs have uniform temporalrepresentations. Therefore, we propose a simple graph neural network (GNN)model combined with a temporal information matching mechanism, which achievesbetter performance with less time and fewer parameters. Furthermore, sincealignment seeds are difficult to label in real-world applications, we alsopropose a method to generate unsupervised alignment seeds via the temporalinformation of TKG. Extensive experiments on public datasets indicate that oursupervised method significantly outperforms the previous methods and theunsupervised one has competitive performance.

A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge Graphs