RGM-KGC: A Multi-Stage Framework for LLMs-Based Knowledge Graph Completion

Knowledge Graph (KG) faces challenges of incompleteness, driving the requirements of Knowledge Graph Completion (KGC). The development of Large Language Models (LLMs) provides a new perspective for KGC research. Several methods instruct LLMs to conduct KGC by Prompt Engineering. However, they struggle with missing entity/relation descriptions, “text mismatch” between LLMs' responses and entities in KG, and insufficient utilization of structural information. To address the above challenges, we propose RGM-KGC, a multi-stage method for LLMs-based KGC. It first retrieves a subgraph related to query, with the aim to provide structured information of entities for LLMs. Subsequently, in order to enrich the semantics of entities and relations, RGM-KGC guides LLMs via CoT prompts in generating additional knowledge based on the query and its subgraph, thereby enhancing LLMs' cognition. Finally, RGM-KGC conducts similarity matching between LLMs' responses and the entities in knowledge graph to obtain candidate entities, with the aim to solve “text mismatch” problem. We conduct comprehensive experiments on the link prediction task using two benchmark datasets, FB15K-237-N and Wiki27K, which proved the effectiveness of RGM-KGC.