A Probabilistic Inference Based Approach for Querying Associative Entities in Knowledge Graph

Jianyu Li, Kun Yue, Jie Li, Liang Duan

Abstract: Querying associative entities is to provide top ranked entities in knowledge graph (KG). Many entities are not linked explicitly in KG but actually associated when incorporating outside user-generated data, which could enrich entity associations for the query processing of KG. In this paper, we leverage user-entity interactions (called user-entity data) to improve the accuracy of querying associative entities in KG. Upon the association rules obtained from user-entity data, we construct the association entity Bayesian network (AEBN), which facilitates the representation and inference of the dependencies among entities. Consequently, we formulate the problem of querying associative entities as the probabilistic inferences over AEBN. To rank the associative entities, we propose the approximate method to evaluate the association degree between entities. Extensive experiments on various datasets verify the effectiveness and efficiency of our method. Experimental results show that our proposed method outperforms some state-of-the-art competitors.