Research on knowledge reasoning of TCM based on knowledge graphs  

基于知识图谱的中医药知识推理研究

作　　者：GUO Zhiheng LIU Qingping ZOU Beiji 郭志恒;刘青萍;邹北骥(湖南中医药大学信息科学与工程学院,湖南长沙410208;中南大学计算机学院,湖南长沙410083)

机构地区：[1]School of Informatics,Hunan University of Chinese Medicine,Changsha,Hunan 410208,China [2]School of Computer Science and Engineering,Central South University,Changsha,Hunan 410083,China

出　　处：《Digital Chinese Medicine》2022年第4期386-393,共8页数字中医药（英文）

基　　金：The National Key R&D Program of China(2018AAA0102100);Hunan Provincial Department of Education Outstanding Youth Project(22B0385);Open Fund of the Domestic First-class Discipline Construction Project of Chinese Medicine of Hunan University of Chinese Medicine(2018ZYX17);Electronic Science and Technology Discipline Open Fund Project of School of Information Science and Engineering,Hunan University of Chinese Medicine(2018-2);Hunan University of Chinese Medicine Graduate Innovation Project(2022CX122)。

摘　　要：With the widespread use of Internet,the amount of data in the field of traditional Chinese medicine(TCM)is growing exponentially.Consequently,there is much attention on the collection of useful knowledge as well as its effective organization and expression.Knowledge graphs have thus emerged,and knowledge reasoning based on this tool has become one of the hot spots of research.This paper first presents a brief introduction to the development of knowledge graphs and knowledge reasoning,and explores the significance of knowledge reasoning.Secondly,the mainstream knowledge reasoning methods,including knowledge reasoning based on traditional rules,knowledge reasoning based on distributed feature representation,and knowledge reasoning based on neural networks are introduced.Then,using stroke as an example,the knowledge reasoning methods are expounded,the principles and characteristics of commonly used knowledge reasoning methods are summarized,and the research and applications of knowledge reasoning techniques in TCM in recent years are sorted out.Finally,we summarize the problems faced in the development of knowledge reasoning in TCM,and put forward the importance of constructing a knowledge reasoning model suitable for the field of TCM.随着互联网技术的广泛应用,中医药行业领域数据规模呈指数型增长,如何从中筛选出有用的知识并有效组织和表达备受关注。知识图谱由此而生,基于知识图谱的知识推理成为研究的热点之一。本文首先简要介绍知识图谱和知识推理的发展及探讨知识推理的意义。其次,介绍主流的知识推理方法分类,包括基于传统规则的推理、基于分布式特征表示的推理、基于神经网络的推理。再以脑卒中疾病为实例,对知识推理方法进行阐述,总结常用知识推理方法的原理及特点,并梳理近些年知识推理技术在中医药领域的研究与应用。最后,总结中医药知识推理发展所面临的问题,提出构建适合中医药领域的知识推理模型的重要性。

关 键 词：Traditional Chinese medicine(TCM) STROKE Knowledge graph Knowledge reasoning Assisted decision-making Transloction Embedding(TransE)model 

分 类 号：R2-03[医药卫生—中医学] TP391.1[自动化与计算机技术—计算机应用技术]