  Entity alignment (EA) aims to find entities in different knowledge graphs
(KGs) that refer to the same object in the real world. Recent studies
incorporate temporal information to augment the representations of KGs. The
existing methods for EA between temporal KGs (TKGs) utilize a time-aware
attention mechanism to incorporate relational and temporal information into
entity embeddings. The approaches outperform the previous methods by using
temporal information. However, we believe that it is not necessary to learn the
embeddings of temporal information in KGs since most TKGs have uniform temporal
representations. Therefore, we propose a simple graph neural network (GNN)
model combined with a temporal information matching mechanism, which achieves
better performance with less time and fewer parameters. Furthermore, since
alignment seeds are difficult to label in real-world applications, we also
propose a method to generate unsupervised alignment seeds via the temporal
information of TKG. Extensive experiments on public datasets indicate that our
supervised method significantly outperforms the previous methods and the
unsupervised one has competitive performance.
