Year: 2024
Type: preprint
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.
Source: arXiv (Cornell University)
Institutions: University of Lübeck, Abbès Laghrour University of Khenchela, Université de Toulouse, Université Paris Cité, Alliance University, Autonomous University of Tamaulipas, IBM (United States), IBM Research - Thomas J. Watson Research Center, Université Paris Descartes, Laboratoire Informatique Paris Descartes, Institute for Infocomm Research, Image and Pervasive Access Laboratory, Institut de Recherche en Informatique de Toulouse
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W4399657455