  While Large Language Models (LLMs) demonstrate exceptional performance in a
multitude of Natural Language Processing (NLP) tasks, they encounter challenges
in practical applications, including issues with hallucinations, inadequate
knowledge updating, and limited transparency in the reasoning process. To
overcome these limitations, this study innovatively proposes a collaborative
training-free reasoning scheme involving tight cooperation between Knowledge
Graph (KG) and LLMs. This scheme first involves using LLMs to iteratively
explore KG, selectively retrieving a task-relevant knowledge subgraph to
support reasoning. The LLMs are then guided to further combine inherent
implicit knowledge to reason on the subgraph while explicitly elucidating the
reasoning process. Through such a cooperative approach, our scheme achieves
more reliable knowledge-based reasoning and facilitates the tracing of the
reasoning results. Experimental results show that our scheme significantly
progressed across multiple datasets, notably achieving over a 10% improvement
on the QALD10 dataset compared to the best baseline and the fine-tuned
state-of-the-art (SOTA) work. Building on this success, this study hopes to
offer a valuable reference for future research in the fusion of KG and LLMs,
thereby enhancing LLMs' proficiency in solving complex issues.
