Enhancing Domain-Specific Knowledge Graph Reasoning via Metapath-Based Large Model Prompt Learning

Representing domain knowledge extracted from unstructured texts using knowledge graphs supports knowledge reasoning, enabling the extraction of accurate factual information and the generation of interpretable results. However, reasoning with knowledge graphs is challenging due to their complex logical structures, which require deep semantic understanding and the ability to address uncertainties with common sense. The rapid development of large language models makes them an option for solving this problem, with good complementary capabilities regarding the determinacy of knowledge graph reasoning. However, the use of large language models for knowledge graph reasoning also has challenges, including structural understanding challenges and the balance of semantic density sparsity. This study proposes a domain knowledge graph reasoning method based on a large model prompt learning metapath (DKGM-path), discussing how to use large models for the preliminary induction of reasoning paths and completing reasoning on knowledge graphs based on iterative queries. The method has made significant progress on several public reasoning question answering benchmark datasets, demonstrating multi-hop reasoning capabilities based on knowledge graphs. It utilizes structured data interfaces to achieve accurate and effective data access and information processing and can intuitively show the reasoning process, with good interpretability.