TypeEA: Type-Associated Embedding for Knowledge Graph Entity Alignment

Xiou Ge, Yun-Cheng Wang, Bin Wang, C.-C. Jay Kuo

Abstract: Entity alignment is commonly used to link different knowl- edge graphs and augment facts about entities. The main objective is to identify the counterpart of a source entity in the target knowledge graph. Although the auxiliary in- formation such as textual, visual, and temporal features was leveraged to improve the entity alignment performance in the past, the entity type information is rarely consid- ered in existing entity alignment models. In this paper, we demonstrate that the entity type information, which is commonly available in knowledge graphs, is very helpful to knowledge graph alignment and propose a new method called the Type-associated Entity Alignment (TypeEA) ac- cordingly. TypeEA exploits the entity type information to guide entity alignment models so that they can focus on entities with matching types. A type embedding model based on semantic matching is developed in TypeEA to capture the association between types in different knowl- edge graphs. Experimental results show that the proposed TypeEA consistently outperforms state-of-the-art baselines across all OpenEA entity alignment datasets with different experimental settings.