REVIEW article
Front. Comput. Sci.
Sec. Human-Media Interaction
Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1590632
Practices, Opportunities and Challenges in the Fusion of Knowledge Graphs and Large Language Models
Provisionally accepted
- 1Sichuan University, Chengdu, China
- 2Zhejiang University of Finance \& Economics Dongfang College, Jiaxing, China
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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.
Keywords: Large Language Model, knowledge graph, KEL, lek, LKC
Received: 10 Mar 2025; Accepted: 25 Jun 2025.
Copyright: © 2025 Cai, Yu, Kang, Fu, Zhang and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yong Zhao, Sichuan University, Chengdu, China
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