大数据2025，Vol.11Issue(2)：19-28,10.DOI:10.11959/j.issn.2096-0271.2025022
代码大语言模型赋能的知识图谱关键技术综述
Review of key technologies in knowledge graphs powered by code large language models
摘要
Abstract
Traditional knowledge graph technologies still face significant challenges in converting human knowledge,expressed in natural language,into a formal language-based knowledge graph and utilizing it effectively.In recent years,code large language models(LLMs)have demonstrated remarkable capabilities in understanding both natural and formal languages,as well as in translating between them.These advancements are expected to drive significant breakthroughs in developing next-generation knowledge graph technologies.This paper reviews the application of code LLMs in KGs.Firstly,this paper systematically analyzes the role of code LLMs in enhancing key knowledge graph technologies across three critical areas:construction,reasoning,and question-answering.Secondly,a relatively detailed introduction to the existing methodologies in these areas is provided.Finally,this paper summarizes the current state in the field and offers insights into the future of knowledge graph technologies empowered by code LLMs.In the future,knowledge representation based on programming languages is expected to enable more efficient,automated,and complex operations on knowledge graphs,realizing knowledge programming.关键词
知识图谱/代码大语言模型/大语言模型Key words
knowledge graph/code large language model/large language model分类
计算机与自动化引用本文复制引用
李紫宣,白龙,任韦澄,苏淼,刘文轩,陈磊,靳小龙..代码大语言模型赋能的知识图谱关键技术综述[J].大数据,2025,11(2):19-28,10.基金项目
国家自然科学基金项目(No.62306299,No.62406308) The National Natural Science Foundation of China(No.62306299,No.62406308) （No.62306299,No.62406308）
