Research Trends for the Interplay between Large Language Models and Knowledge Graphs
This is an incremental survey that addresses gaps in current research on LLM-KG interactions for improving AI applications in understanding and reasoning.
This survey tackles the problem of understanding and advancing the synergistic relationship between Large Language Models (LLMs) and Knowledge Graphs (KGs) to enhance AI capabilities in areas like reasoning and language processing, by exploring gaps in research such as KG Question Answering and ontology generation, and providing structured analysis and future directions.
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.