Research Trends for the Interplay between Large Language Models and Knowledge Graphs

TL;DR

This survey explores the evolving relationship between Large Language Models and Knowledge Graphs, highlighting their combined potential to enhance AI reasoning, understanding, and language processing through various collaborative techniques.

Contribution

It provides a comprehensive categorization and analysis of LLM-KG interactions, methodologies, and future research directions, addressing current research gaps.

Findings

LLMs can improve KG question answering and validation

Enhanced KG accuracy and consistency through LLMs

Identification of biases in LLM-KG interactions

Abstract

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.