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

摘要： 为了有效发现实体间隐含的关联关系并对其进行量化,以全面准确地进行知识图谱(KG)链接预测,提出基于概率推理KG链接预测方法。该方法以描述实体间隐含关联关系并度量链接存在的可能性为目标,基于AMIE算法挖掘KG中的规则并将其转换为Horn子句,进一步构建描述不同实体依赖关系的规则链接贝叶斯网(RLBN),将KG的链接预测任务转换为RLBN的概率推理任务来计算实体间的关联度,从而预测实体间的链接关系。实验结果表明,基于RLBN的KG链接预测精确率和召回率优于其他方法,验证了所提模型的有效性与高效性。

关键词: 知识图谱, 链接预测, 贝叶斯网, Horn子句, 概率推理