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ORIGINAL RESEARCH article
Front. Pharmacol.
Sec. Ethnopharmacology
Volume 16 - 2025 |
doi: 10.3389/fphar.2025.1535596
This article is part of the Research Topic Artificial Intelligence in Traditional Medicine Research and Application View all 6 articles
TCMRD -KG： Innovative Design and Development of Rheumatology Knowledge Graph in Ancient Chinese Literature Assisted by Large Language Models
Provisionally accepted- 1 School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
- 2 Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, Beijing Municipality, China
- 3 Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
- 4 Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Science, Dongcheng, China
Rheumatic immune diseases are a type of immune-inflammatory disease that affects muscles, bones, joints, and surrounding soft tissues. They have a long course and a high disability rate, seriously affecting the quality of life of patients. Traditional Chinese medicine plays an important role in the diagnosis and treatment of rheumatic immune diseases. The unique theoretical system and rich treatment methods of traditional Chinese medicine are preserved in ancient Chinese medical books. This study takes the content related to rheumatism in ancient traditional Chinese medicine books as the research object, integrates ontology theory and technology into the knowledge graph, and realizes the reconstruction of traditional Chinese medicine information knowledge. It provides a basic data structure for data mining and knowledge discovery. This study is the first rheumatism-specific knowledge graph constructed based on ancient traditional Chinese medicine books. It has explored the construction method of a knowledge graph from ancient books by combining automatic labeling of mainstream large language models with manual review. Considering the knowledge characteristics of ancient traditional Chinese medicine books, where existing word segmentation technology struggles to accurately reproduce the original meaning, a new type of entity extraction method is proposed. This provides an important foundation for improving the clinical diagnosis and treatment level of traditional Chinese medicine in treating rheumatism, further exploring the knowledge representation and application of traditional Chinese medicine in rheumatism treatment, and it has potential for future expansion and improvement.
Keywords: LLMS, TCM (trad. chinese medicine), Rheumatic, Knowledge Graph (KG), Artifcial intelligence, Data managament
Received: 27 Nov 2024; Accepted: 27 Jan 2025.
Copyright: © 2025 Li, Xia, Hou, Hu, Liu and Quan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yanjun Liu, Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Science, Dongcheng, China
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