知识图谱推理是解决知识图谱不完整性的重要手段之一.针对现有基于嵌入的推理模型依赖准确事实,可解释性较差,而基于规则的推理模型过于依赖图谱的完整性,数据稀疏时推理性能较低,无法准确表达推理模式.因此文中提出联合规则推理模式和事实嵌入的知识图谱推理模型(Knowledge Graph Reasoning Combining Rule Inference Patterns and Fact Embedding,RPFE).首先,将BoxE作为基础嵌入模型,实现事实的嵌入表示.再设计推理模式差异性函数,辅助嵌入模型捕获不同推理模式的规则,并对规则学习提供直观的嵌入解释.然后,提出事实距离一致性评分函数,强化嵌入表示.最后,优化规则和事实得分,弥补知识图谱高质量事实不足的缺陷,进而提升模型推理的可解释性.在3个公开数据集上的实验表明RPFE在知识图谱推理方面的优越性.
Knowledge Graph Reasoning Combining Rule Inference Patterns and Fact Embedding
Knowledge graph reasoning is an essential approach to address the incompleteness of knowledge graphs.The existing embedding-based reasoning models rely on accurate facts and suffer from poor interpretability.Rule-based reasoning models depend on the completeness of knowledge graphs,resulting in low inference performance on sparse data and an inability to express inference patterns accurately.To address these issues,a model of knowledge graph reasoning combining rule inference patterns and fact embedding(RPFE)is proposed.First,BoxE is employed as the base embedding model to achieve the embedding representation of facts.Second,the inference pattern diversity functions are designed to assist the embedding models in capturing the rules of different inference patterns,providing intuitive embedded interpretation for rule learning.Then,the fact distance consistency scoring functions are proposed to enhance the embedding representation.Finally,the rules and fact scores are optimized to compensate the lack of high-quality facts in knowledge graphs and improve the interpretability of the reasoning.Experiments on three public datasets indicate that the RPFE yields excellent performance in knowledge graph reasoning.
万方数据
维普
