大模型与知识图谱互增技术与应用综述

doi:10.11896/jsjkx.250700129

摘要/Abstract

摘要： 随着人工智能的发展,大模型与知识图谱在自然语言处理领域的能力得到广泛关注。大模型凭借强大的自然语言理解与生成能力,在开放域问答、文本创作等任务中展现出惊人的能力。然而,大模型面临着可解释性不足、知识表达存在幻觉等问题;知识图谱结构化的知识表达为复杂推理与决策提供可解释的符号化支撑,但存在构建成本高、内容不完整等问题。因此,大模型与知识图谱的互增技术成为重点研究方向。对此,系统梳理了大模型与知识图谱的相关知识,介绍了大模型与知识图谱互增技术,包括知识图谱增强大模型、大模型增强知识图谱、知识图谱与大模型互增协同。此外,对大模型与知识图谱互增协同应用进行了阐述,最后总结了大模型与知识图谱互增的挑战与展望,为大模型与知识图谱的互增研究提供借鉴。

关键词: 大语言模型, 知识图谱, 互增协同, 自然语言处理, 可解释

Abstract: With the development of artificial intelligence,the capabilities of large language models(LLMs) and knowledge graphs(KGs) in the field of natural language processing gained a lot of attention.Endowed with robust natural language understanding and generation capacities,LLMs have exhibited prominent emergent abilities in tasks including open-domain question answering and text generation.Nevertheless,LLMs are confronted with challenges such as inadequate interpretability and the existence of hallucinations in knowledge representation.Similarly,knowledge graphs provide interpretable symbolic support for complex reasoning and decision-making through structured knowledge representation,yet suffer from high construction costs and incomplete content.Therefore,the mutual enhancement technology of large language models and knowledge graphs has become a key research direction.This paper systematically reviews the relevant knowledge of large language models and knowledge graphs and introduces the mutual enhancement technologies between large language models and knowledge graphs,including knowledge graph-enhanced large models,large language model-enhanced knowledge graphs,and the mutual enhancement and collaboration between large language models and knowledge graphs.Additionally,it elaborates on the application of this synergistic framework.Finally,it summarizes the challenges and prospects of the mutually reinforced large language model-knowledge graph system,providing references for subsequent studies of this technology.

Key words: Large language model, Knowledge graph, Mutual enhancement and synergy, Natural language processing, Explicable

中图分类号:

TP391.1

贾子硕, 张俭鸽, 贺浩峰, 冯世忠, 刘伊琳. 大模型与知识图谱互增技术与应用综述[J]. 计算机科学, 2026, 53(8): 219-228. https://doi.org/10.11896/jsjkx.250700129

JIA Zishuo, ZHANG Jian’ge, HE Haofeng, FENG Shizhong, LIU Yilin. Survey on Mutually Augmenting Technologies and Applications of Large Models and KnowledgeGraphs[J]. Computer Science, 2026, 53(8): 219-228. https://doi.org/10.11896/jsjkx.250700129

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