Embedding-Based Entity Alignment of Cross-Lingual Temporal Knowledge Graphs.

Entity alignment aims to construct a complete knowledge graph (KG) by matching the same entities in multi-source KGs. Existing researches on entity alignment mainly focuses on static multi-relational data in knowledge graphs. However, the relationships or attributes between entities often possess temporal characteristics as well. Neglecting these temporal characteristics can frequently lead to alignment errors. Compared to studying entity alignment in temporal knowledge graphs, there are relatively few efforts on entity alignment in cross-lingual temporal knowledge graphs. Therefore, in this paper, we put forward an entity alignment method for cross-lingual temporal knowledge graphs, namely CTEA. Based on GCN and TransE, CTEA combines entity embeddings, relation embeddings and attribute embeddings to design a joint embedding model, which is more conducive to generating transferable entity embedding. In the meantime, the distance calculation between elements and the similarity calculation of entity pairs are combined to enhance the reliability of cross-lingual entity alignment. Experiments shows that the proposed CTEA model improves Hits@m and MRR by about 0.8∼2.4 percentage points compared with the latest methods.