首页|基于BSG深度学习模型的中医药智能问答系统研究:以方剂和中药为例

基于BSG深度学习模型的中医药智能问答系统研究:以方剂和中药为例

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
目的 基于深度学习的方法,利用知识图谱构建中医药知识库,探寻联合模型在中医药智能问答系统的应用.方法 以《方剂学》和《中药学》规划教材为基础建立知识图谱,作为智能问答系统的知识来源,本研究提出一种BERT+Slot-Gated(BSG)深度学习模型,获取用户自然问题中包含的中医药实体和问句意图,通过实体和意图在知识图谱检索答案返回给用户,运用Flask框架和BSG模型研发中医药智能问答系统.结果 构建了包含3 149个实体和6 891个关系三元组的中医药知识图谱,通过问题语料测试,本系统在回答中医药 20 类问题的实体识别F1 值为 0.996 9,意图识别准确率为 99.75%,表明本系统具有较高的实用性和可行性,并通过微信公众号平台实现了用户与系统交互.结论 本文所提出的BSG模型通过提高向量维度在实验中取得了较好的结果,表明联合模型方法的有效性,可为实现中医药智能问答系统提供新的研究思路.
Intelligent question answering system for traditional Chinese medicine based on BSG deep learning model:taking prescription and Chinese materia medica as examples
Objective To construct a traditional Chinese medicine(TCM)knowledge base using knowl-edge graph based on deep learning methods,and to explore the application of joint models in intelligent question answering systems for TCM.Methods Textbooks Prescriptions of Chinese Materia Medica and Chinese Materia Medicawere applied to construct a comprehensive knowledge graph serving as the founda-tion for the intelligent question answering system.In the study,a BERT+Slot-Gated(BSG)deep learning model was applied for the identification of TCM entities and question inten-tions presented by users in their questions.Answers retrieved from the knowledge graph based on the identified entities and intentions were then returned to the user.The Flask framework and BSG model were utilized to develop the intelligent question answering sys-tem of TCM.Results A TCM knowledge map encompassing 3 149 entities and 6 891 relational triples based on the prescriptions and Chinese materia medica was drawn.In the question answer-ing test assisted by a question corpus,the F1 value for recognizing entities when answering 20 types of TCM questions was 0.996 9,and the accuracy rate for identifying intentions was 99.75%.This indicates that the system is both feasible and practical.Users can interact with the system through the WeChat Official Account platform.Conclusion The BSG model proposed in this paper achieved good results in experiments by increasing the vector dimension,indicating the effectiveness of the joint model method and providing new research ideas for the implementation of intelligent question answering sys-tems in TCM.

Traditional Chinese medicine(TCM)Deep learningKnowledge graphIntelligent question answering systemBERT+Slot-Gated(BSG)model

李冉、任高、晏峻峰、邹北骥、刘青萍

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湖南中医药大学信息科学与工程学院,湖南长沙 410208,中国

中南大学计算机学院,湖南长沙 410083,中国

中医药 深度学习 知识图谱 智能问答系统 BERT+Slot-Gated模型

国家重点研发计划Hunan Provincial Department of Education Outstanding Youth ProjectDisciplinary Construction"Revealing the List and Appointing Leaders"Project(2022)

2018AAA010210022B038522JBZ051

2024

10.1016/j.dcmed.2024.04.006

数字中医药(英文)
湖南中医药大学,中华中医药学会

数字中医药(英文)

ISSN：2096-479X
年,卷(期)：2024.7(1)
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