Research on the construction of a knowledge graph of COVID-19 based on Chinese medicine prescriptions
- DOI
- 10.2991/978-94-6463-102-9_19How to use a DOI?
- Keywords
- COVID-19; Neo4j; Traditional Chinese medicine; Knowledge graph; Prescription
- Abstract
In order to explore the method and general steps of knowledge mapping for mining the prescriptions of novel coronavirus, and to verify the applicability of knowledge mapping in the diagnosis and treatment of novel coronavirus. In this paper, we collected 75 TCM prescriptions published by national and local TCM administrations, and obtained data on the names, tongues and pulses of TCM. The data were analyzed based on the Neo4j database, and the knowledge graphs were constructed based on the rules of association. The research results, on the one hand, achieved explicit expression of the implicit knowledge of Chinese medicine in the prevention and treatment of neocrown pneumonia, such as drugs, symptoms and correlations, and provided a basis for the future construction of an intelligent auxiliary information system based on knowledge mapping, and provided ideas for the application of knowledge mapping in other public health events.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Shiwen Wang AU - Jin Liu AU - Mingxue Li PY - 2022 DA - 2022/12/29 TI - Research on the construction of a knowledge graph of COVID-19 based on Chinese medicine prescriptions BT - Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022) PB - Atlantis Press SP - 155 EP - 167 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-102-9_19 DO - 10.2991/978-94-6463-102-9_19 ID - Wang2022 ER -