基于大规模语言模型的知识图谱可微规则抽取

Knowledge graph (KG) reasoning is to predict missing entities or relationships in incomplete triples, complete structured knowledge, and apply to different downstream tasks. Different from black-box methods which are widely studied, such as methods based on representation learning, the method based on rule extraction achieves an interpretable reasoning paradigm by generalizing first-order logic rules from the KG. To address the gap between discrete symbolic space and continuous embedding space, a differentiable rule extracting method based on the large pre-trained language model (DRaM) is proposed, which integrates discrete first-order logical rules with continuous vector space. In view of the influence of atom sequences in first-order logic rules for the reasoning process, a large pre-trained language model is introduced to encode the reasoning process. The differentiable method DRaM, which integrates first-order logical rules, achieves good results in link prediction tasks on three knowledge graph datasets, Family, Kinship and UMLS, especially for the indicator Hits@10. Comprehensive experimental results show that DRaM can effectively solve the problems of differentiable reasoning on the KGs, and can extract first-order logic rules with confidences from the reasoning process. DRaM not only enhances the reasoning performance with the help of first-order logic rules, but also enhances the interpretability of the method.