A Framework of Knowledge Graph-Enhanced Large Language Model Based on Global Planning

Knowledge graphs (KGs) can provide structured knowledge to assist large language models (LLMs) in interpretable reasoning. Knowledge graph question answering (KGQA) is a typical benchmark to evaluate KG-enhanced LLM methods. Previous methods of KG-enhanced LLMs for KGQA mainly include: 1) origin question-oriented methods, which perform KG retrieval based solely on the original question without explicitly analyzing multi-step reasoning logic; and 2) stepwise reasoning-oriented methods, which alternate between LLM generating the next reasoning step and targeted KG retrieval but lack systematic planning, leading to poor controllability. To tackle these limitations, we propose KELGoP, a framework of KG-enhanced LLM based on global planning. We propose fine-grained question categorization based on reasoning patterns and corresponding category-driven question decomposition for complex questions, enabling more controllable reasoning and atomic KG retrieval targeted to sub-questions. Furthermore, we propose an adaptive strategy that allows adjusting the reasoning pattern based on the performance of question answering, making the reasoning more flexible and robust. Finally, we introduce several efficient atomic KG retrieval strategies that operate on KG subgraphs to assist the LLM in answering atomic-level questions. A series of experiments on KGQA datasets demonstrate that our proposed framework achieves superior performance compared to existing baselines.