Knowledge Graph Combined with Retrieval-Augmented Generation for Enhancing LMs Reasoning: A Survey

Large language models (LLMs) have generated significant waves in the fields of Natural Language Processing(NLP) and artificial intelligence due to their remarkable capabilities and broad adaptability. Retrieval-Augmented Generation (RAG) techniques have been widely adopted, leveraging external retrieval systems to significantly improve the timeliness of LLMs and substantially reduce hallucinations. Although RAG and its optimization methods have addressed most hallucination issues caused by knowledge gaps and outdated information, text generation in specialized domains such as law, medicine, and science—which require multi-hop reasoning and analysis—still suffers from a lack of coherence and logical consistency, making it difficult to produce correct and valuable answers. To address these challenges, related research has introduced Knowledge Graphs (KGs). This integrated approach enhances RAG’s capabilities by utilizing the structured knowledge provided by KGs, thereby improving the model's knowledge representation and reasoning abilities and enabling the generation of more accurate answers. However, there remains a lack of systematic reviews in this area. Therefore, this paper provides a comprehensive review of studies on enhancing LLMs reasoning abilities by integrating KGs with RAG. It first introduces the basic concepts, followed by an overview of the current mainstream technical approaches, and concludes with a discussion of the research challenges and future development trends in this field.