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.

翻译：本综述研究了大语言模型（LLMs）与知识图谱（KGs）之间的协同关系，这对于推进人工智能在理解、推理和语言处理方面的能力至关重要。它旨在通过探索知识图谱问答、本体生成、知识图谱验证以及利用大语言模型提升知识图谱的准确性与一致性等领域，弥补当前研究的空白。本文进一步探讨了大语言模型在生成知识图谱的描述性文本和自然语言查询方面的作用。通过结构化分析，包括对大语言模型与知识图谱交互进行分类、审视方法论、探究协作使用及潜在偏见，本研究旨在为两者结合的潜力提供新的见解。它强调了二者交互对于改进人工智能应用的重要性，并勾勒了未来的研究方向。