Open Access

REVIEW

A Review of Knowledge Graph in Traditional Chinese Medicine: Analysis, Construction, Application and Prospects

by Xiaolong Qu^1,2,3, Ziwei Tian^1,3, Jinman Cui^1,3, Ruowei Li^1,3, Dongmei Li^1,3,*, Xiaoping Zhang^2,*

1 School of Information Science and Technology, Beijing Forestry University, Beijing, 100083, China
2 National Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China
3 Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing Forestry University, Beijing, 100083, China

* Corresponding Authors: Dongmei Li. Email: ; Xiaoping Zhang. Email:

Computers, Materials & Continua 2024, 81(3), 3583-3616. https://doi.org/10.32604/cmc.2024.055671

Received 03 July 2024; Accepted 29 September 2024; Issue published 19 December 2024

Abstract

As an advanced data science technology, the knowledge graph systematically integrates and displays the knowledge framework within the field of traditional Chinese medicine (TCM). This not only contributes to a deeper comprehension of traditional Chinese medical theories but also provides robust support for the intelligent decision systems and medical applications of TCM. Against this backdrop, this paper aims to systematically review the current status and development trends of TCM knowledge graphs, offering theoretical and technical foundations to facilitate the inheritance, innovation, and integrated development of TCM. Firstly, we introduce the relevant concepts and research status of TCM knowledge graphs. Secondly, we conduct an in-depth analysis of the challenges and trends faced by key technologies in TCM knowledge graph construction, such as knowledge representation, extraction, fusion, and reasoning, and classifies typical knowledge graphs in various subfields of TCM. Next, we comprehensively outline the current medical applications of TCM knowledge graphs in areas such as information retrieval, diagnosis, question answering, recommendation, and knowledge mining. Finally, the current research status and future directions of TCM knowledge graphs are concluded and discussed. We believe this paper contributes to a deeper understanding of the research dynamics in TCM knowledge graphs and provides essential references for scholars in related fields.

---

Keywords

---