Entity Alignment Between Knowledge Graphs Using Entity Type Matching.

The task of entity alignment between knowledge graphs (KGs) aims to find entities in two knowledge graphs that represent the same real-world entity. Recently, embedding-based entity alignment methods get extended attention. Most of them firstly embed the entities in low dimensional vectors space via relation structure, and then align entities via these learned embeddings combined with some entity similarity function. Even achieved promising performances, these methods are inadequate in utilizing entity type information. In this paper, we propose a novel entity alignment framework, which integrates entity embeddings and entity type information to achieve entity alignment. This framework uses encoding functions to extract the type features of entities for type matching, and combines the similarity of entity embeddings to improve the accuracy of entity alignment. Our experimental results on several real-world datasets shows that our proposed method achieves improvements on entity alignment compared with most methods, and is close to the state-of-the-art method on several metrics.