关键词: 实体对齐, 可解释性, 邻域结构, 匹配概率, 类型商图

Abstract: In recent years,a variety of embedding-based entity alignment methods have emerged,which achieve entity alignment by mapping entities and relations into low-dimensional vector spaces and calculating these vector representations.Although these methods have achieved good performance,there are relatively few studies on their explainability.Therefore,a post hoc explanation algorithm for entity alignment(PE-EA) is proposed to generate explanations for the prediction results of the entity alignment model based on knowledge graph embedding.The method first evaluates the functionality of entities and their relations by calculating their connection counts in the knowledge graph,and then quantifies the importance of the entity neighborhood structure.Subsequently,the entity functionality is combined with the embedding vector to calculate the embedding vector of the relation,and the matching probability of the relation pair is obtained based on it.Then,based on the neighborhood information of the predicted entity pair,the matching probability of the candidate explanation pairs in the neighborhood is calculated,and the explanation triplets of the predicted entity pair are screened out and combined into an explanation subgraph.Furthermore,the concept of type quotient graph is introduced to abstract the explanatory subgraph,compress the data and simplify the explanation generation process,thereby reducing the number of candidate explanations while enhancing the quality and effectiveness of the explanations.On five commonly used experimental datasets,the two evaluation indicators of fidelity and sparsity are used to verify that the explanations generated by the proposed model have high accuracy and simplicity.

Key words: Entity alignment, Explainability, Neighborhood structure, Probabilistic mathching, Type quotient graph