Work

Practices, opportunities and challenges in the fusion of knowledge graphs and large language models

Year: 2025

Type: article

Abstract: The fusion of Knowledge Graphs (KGs) and Large Language Models (LLMs) leverages their complementary strengths to address limitations of both technologies. This paper explores integration practices, opportunities, and challenges, focusing on three strategies: KG-enhanced LLMs (KEL), LLM-enhanced KGs (LEK), and collaborative LLMs and KGs (LKC). The study reviews these methodologies, highlighting their potential to enhance knowledge representation, reasoning, and question answering. We comprehensively compile and categorize key challenges such as knowledge acquisition and real-time updates, providing valuable directions for future research. The paper also discusses emerging techniques and applications to advance the synergy between KGs and LLMs. Overall, this work offers a comprehensive overview of the current landscape and the transformative potential of KG-LLM fusion across various domains.

Source: Frontiers in Computer Science

DOI: 10.3389/fcomp.2025.1590632

Authors: Linyue Cai, Chaojia Yu, Yongqi Kang, Yu Fu, Heng Zhang, Yong Zhao

Institutions: Sichuan University, Zhejiang University of Finance and Economics Dongfang College, Zhejiang University of Finance and Economics

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,418

Funder: National Natural Science Foundation of China