Abstract: 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.

Key words: knowledge graph reasoning, first-order logic rule, large language model (LLM), interpretable reasoning

摘要： 知识图谱上的推理是预测不完整三元组中缺失的实体或关系，对结构化知识进行补全，并用于不同下游任务的过程。不同于被普遍研究的黑盒方法，如基于表示学习的推理方法，基于规则抽取的推理方法通过从知识图谱中泛化出一阶逻辑规则，实现一种可解释的推理范式。为解决离散的符号空间与连续的嵌入空间之间的鸿沟，提出一种基于大规模预训练语言模型的知识图谱可微规则抽取方法DRaM，将离散的一阶逻辑规则与连续的向量空间进行融合。针对规则中的原子公式顺序对推理过程产生的影响，通过引入大规模预训练语言模型对推理过程进行编码来解决。融合一阶逻辑规则的可微推理方法DRaM，在三个知识图谱数据集Family、Kinship和UMLS上进行的链接预测任务获得了较好的结果，尤其针对链接预测指标Hits@10，DRaM获得了最佳的推理结果。实验结果表明，DRaM能够有效地解决知识图谱上可微推理存在的问题，并且可以从推理过程中抽取带有置信度的一阶逻辑规则。DRaM不仅通过一阶逻辑规则增强了推理效果，同时增强了方法的可解释性。

关键词: 知识图谱上的推理, 一阶逻辑规则, 大规模语言模型（LLM）, 可解释推理