Research on knowledge reasoning of TCM based on knowledge graphs
10.1016/j.dcmed.2022.12.005
- Author:
Zhiheng GUO
1
;
Qingping LIU
1
;
Beiji ZOU
1
,
2
Author Information
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
- Publication Type:Review
- Keywords:
Traditional Chinese medicine (TCM);
stroke;
knowledge graph;
knowledge reasoning;
assisted decision-making;
Transloction Embedding (TransE) model
- From:
Digital Chinese Medicine
2022;5(4):386-393
- CountryChina
- Language:English
-
Abstract:
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.
- Full text:liuqingping.pdf
