  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.
