关键词: 知识图谱, 链接预测, 贝叶斯网, Horn子句, 概率推理

Abstract: The knowledge in Knowledge Graph (KG)is incomplete and there are missing links between some entities.To effectively discover the implicit association between entities and quantify them for fulfilling KG link prediction comprehensively and accurately,a prediction method based on probabilistic inference was proposed,which aimed to describe the implicit association relationships between entities and measure the possibility of links.Based on AMIE algorithm,rules in KG were obtained and transformed into Horn clauses to further build a Rule-Linked Bayesian Network (RLBN)describing different entity dependencies.Link prediction of KG was transformed into the probabilistic inference over the RLBN to calculate the correlation degree between entities,so as to predict the link relationship between entities.Experimental results showed that the accuracy and recall of the RLBN based link prediction results was better than other competitors,which verified the effectiveness and efficiency of the proposed model.

Key words: knowledge graph, link prediction, Bayesian network, Horn clause, probabilistic inference