计算机工程与应用2025，Vol.61Issue(4)：167-175,9.DOI:10.3778/j.issn.1002-8331.2403-0379
大语言模型构建鼻炎医案知识图谱的应用研究
Study on Application of Large Language Model in Constructing Knowledge Graph of Medical Cases of Rhinitis
摘要
Abstract
An automated knowledge extraction method based on large language model is explored,aiming to construct a knowledge graph on the treatment of rhinitis by national medical master Gan Zuwang,and to provide innovative ideas and methods for the intelligent advancement in the field of traditional Chinese medicine.The clinical medical case data of pro-fessor Gan Zuwang are used as the base sample,and the ontology model is constructed using OWL(Web ontology lan-guage)to determine the extraction objects and relations,and then the prompt template combining the demonstration case and the relation list is used to guide the automated extraction experiments of the medical case data with the large language model,and the Nebula Graph is used for the storage and the visual display of the knowledge graph.Compared with the tra-ditional knowledge extraction model Bert-BiLSTM-CRF,the ChatGPT4 model performs the best in terms of comprehen-sive indexes,with an F1 value of 82.75%,which provides an effective solution for the rapid processing of unstructured medical case data and achieves semi-automatic construction of knowledge graph in the field of Chinese medicine.The use of large language models for knowledge graph construction not only provides a practical solution for the intelligence in the field of Chinese medicine,but also contributes new research ideas for the inheritance of diagnostic and treatment expe-rience of famous and veteran Chinese medicine practitioners and the rapid construction of the knowledge graph of Chinese medicine,which promotes the development of Chinese medicine.关键词
国医大师/干祖望/大语言模型/Nebula GraphKey words
national medical master/Gan Zuwang/large language model/Nebula Graph分类
中医学引用本文复制引用
李玥,洪海蓝,李文林,杨涛..大语言模型构建鼻炎医案知识图谱的应用研究[J].计算机工程与应用,2025,61(4):167-175,9.基金项目
江苏省中医药管理局2021年度江苏省中医药科技发展计划(ZT202101). （ZT202101）
