This survey investigates the synergistic relationship between Large Language
Models (LLMs) and Knowledge Graphs (KGs), which is crucial for advancing AI's
capabilities in understanding, reasoning, and language processing. It aims to
address gaps in current research by exploring areas such as KG Question
Answering, ontology generation, KG validation, and the enhancement of KG
accuracy and consistency through LLMs. The paper further examines the roles of
LLMs in generating descriptive texts and natural language queries for KGs.
Through a structured analysis that includes categorizing LLM-KG interactions,
examining methodologies, and investigating collaborative uses and potential
biases, this study seeks to provide new insights into the combined potential of
LLMs and KGs. It highlights the importance of their interaction for improving
AI applications and outlines future research directions.

本研究调查了大型语言模型（LLMs）与知识图谱（KGs）之间的协同关系，旨在探讨 KG 问答、本体生成、KG 验证以及通过 LLMs 提高 KG 准确性和一致性等领域的研究空白。该论文还检验了 LLMs 在生成描述性文本和自然语言查询方面的作用，并通过分类 LLM-KG 交互、研究方法论和合作使用以及潜在偏见等结构化分析，提供了它们的结合潜力的新见解。该研究强调了它们的交互对于提升人工智能应用的重要性，并概述了未来的研究方向。