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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 8

Review of large language models integrating knowledge graph

Cao Rongrong1
Liu Lin1
Yu Yandong2
Wang Hailong1
1. School of Computer Science & Technology, Inner Mongolia Normal University, Hohhot Inner Mongolia 010020, China
2. Jining Normal University, Wulanchabu City Intelligent Information Processing & Security Key Laboratory, Wulanchabu Inner Mongolia 012000, China
DOI: 10.19734/j.issn.1001-3695.2024.12.0532

Abstract

Large language models (LLMs) have demonstrated exceptional performance across multiple vertical domains, yet their practical deployment remains constrained by limited explainability and hallucination issues in generated content. Knowledge graphs (KGs) , which store factual knowledge in structured semantic networks, provide a novel pathway to enhance the controllability and knowledge constraints of LLMs. To address these challenges, this study systematically reviews technical approaches for integrating KGs with LLMs. We analyze representative methods across three key stages—pretraining adaptation, architectural modification, and fine-tuning optimization—and summarize their mechanisms for improving model explainability and suppressing hallucinations. Furthermore, we identify core challenges such as multimodal knowledge representation alignment and latency in dynamic knowledge integration. Our analysis reveals that deep integration of KGs significantly enhances the factual consistency of LLM-generated content. However, future research must overcome critical technical bottlenecks in multimodal knowledge alignment, lightweight incremental fusion, and complex reasoning verification to shift LLMs from language-centric to knowledge-augmented paradigms, thereby establishing theoretical and technical foundations for building trustworthy and interpretable AI systems.

Foundation Support

内蒙古自治区自然科学基金资助项目(2022QN06003,2023LHMS06006,2024LHMS06015)
内蒙古师范大学基本科研业务费专项基金资助项目(2022JBYJ032)
内蒙古自治区档案馆档案科技项目(2023-13)
无穷维哈密顿系统及其算法应用教育部重点实验室(内蒙古师范大学)(2023KFYB03)
无穷维哈密顿系统及其算法应用教育部重点实验室(内蒙古师范大学)(2023KFZD03)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.12.0532
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 8

Publish History

[2025-04-17] Accepted Paper

Cite This Article

曹荣荣, 柳林, 于艳东, 等. 融合知识图谱的大语言模型研究综述 [J]. 计算机应用研究, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2024.12.0532. (Cao Rongrong, Liu Lin, Yu Yandong, et al. Review of large language models integrating knowledge graph [J]. Application Research of Computers, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2024.12.0532. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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