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CC BY 4.0 · Methods Inf Med 2024; 63(05/06): 183-194
DOI: 10.1055/a-2590-6348
Original Article

TCMSF: A Construction Framework of Traditional Chinese Medicine Syndrome Ancient Book Knowledge Graph

Ziling Zeng   *
1   Materia Medica Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, People's Republic of China
,
Lin Tong   *
2   The Ancient Book Resources Research Office, Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, People's Republic of China
,
Bing Li
3   Materia Medica Digital Intelligence Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, People&s Republic of China
,
Wenjing Zong
4   Integrated Research Center for Chinese Materia Medica, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, People's Republic of China
,
Qikai Niu
4   Integrated Research Center for Chinese Materia Medica, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, People's Republic of China
,
Sihong Liu
5   Department of Special Collections Research and Development, Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, People's Republic of China
,
Lei Zhang
2   The Ancient Book Resources Research Office, Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, People's Republic of China
,
Jialun Wang
1   Materia Medica Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, People's Republic of China
,
Siqi Zhang
1   Materia Medica Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, People's Republic of China
,
Siwei Tian
1   Materia Medica Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, People's Republic of China
,
Jing'ai Wang
1   Materia Medica Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, People's Republic of China
,
Wei Zhang
1   Materia Medica Research Center, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, People's Republic of China
,
Huamin Zhang
6   Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medicine Science, Beijing, People's Republic of China
› Institutsangaben
Funding This study was supported by the National Key R&D Program of China (2023YFC3502900).
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Abstract

Background

Syndrome is a unique and crucial concept in traditional Chinese medicine (TCM). However, much of the syndrome knowledge lacks systematic organization and correlation, and current information technologies are unsuitable for TCM ancient texts.

Objectives

We aimed to develop a knowledge graph that presents this knowledge in a more orderly, structured, and semantically oriented manner, providing a foundation for computer-aided diagnosis and treatment.

Methods

We developed a construction framework of TCM syndrome knowledge from ancient books, using a pretrained model and rules (TCMSF). We conducted fine-tuning training on Enhanced Representation through Knowledge Integration (ERNIE), Bidirectional Encoder Representation from Transformers pretrained language models, and chatGLM3–6b large language models for named entity recognition (NER) tasks. Furthermore, we employed the progressive entity relationship extraction method based on the dual pattern feature combination to extract and standardize entities and relationships between entities in these books.

Results

We selected Yin deficiency syndrome as a case study and constructed a model layer suitable for the expression of knowledge in these books. Compared with multiple NER methods, the combination of ERNIE and Conditional Random Fields performs the best. By utilizing this combination, we completed the entity extraction of Yin deficiency syndrome, achieving an average F1 value of 0.77. The relationship extraction method we proposed reduces the number of incorrectly connected relationships compared with fully connected pattern layers. We successfully constructed a knowledge graph of ancient books on Yin deficiency syndrome, including over 120,000 entities and over 1.18 million relationships.

Conclusion

We developed TCMSF in line with the knowledge characteristics of ancient TCM books and improved the accuracy of knowledge graph construction.

Keywords

TCM - knowledge organization - knowledge graph - ancient book - relationship extraction

* These authors contributed equally to this work.




Publikationsverlauf

Eingereicht: 09. Mai 2024

Angenommen: 09. April 2025

Accepted Manuscript online:
17. April 2025

Artikel online veröffentlicht:
15. Mai 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

 

  • References

  • 1 Guo MY, Zhou L, Sun Y. The application of “domain ontology seven step method” in the construction of traditional Chinese medicine syndrome differentiation and reasoning knowledge base world science and technology. Modern Trad Chin Med 2019; 12: 2646-2651
    Suche in Google Scholar
  • 2 Zhou H, Peng FL, Wei CF. [Research and practice on the construction of a knowledge graph for diagnosis and differentiation of traditional Chinese medicine]. Journal of Medical Informatics 2020; 12: 41-44
    Suche in Google Scholar
  • 3 Zhang MZ, Yang ZG, Liu C, Fang L. Traditional Chinese medicine knowledge service based on semi-supervised BERT-BiLSTM-CRF Model. 2020 International Conference on Service Science (ICSS), Xining, China. 2020: 64-69
    Suche in Google Scholar
  • 4 Sun Y, Zhao Z, Wang Z. et al. Leveraging a joint learning model to extract mixture symptom mentions from traditional Chinese medicine clinical notes. BioMed Res Int 2022; 2022: 2146236
    Suche in Google Scholar
  • 5 Chen K, Wang W, Cai J. TRBNER: named entity recognition of TCM medical records based on multi-feature fusion. IET Conf Proc 2025; 2024 (21) 174-181
    Suche in Google Scholar
  • 6 Jiang M, Sanger T, Liu X. Combining contextualized embeddings and prior knowledge for clinical named entity recognition: evaluation study. JMIR Med Inform 2019; 7 (04) e14850
    Suche in Google Scholar
  • 7 Bao ZS, Song BY, Zhang WB. [Named entity recognition in traditional Chinese medicine books combining semi-supervised learning and rule-based approach.]. J Chinese Information Processing 2022; 6: 90-100
    Suche in Google Scholar
  • 8 Song Z, Xu W, Liu Z, Chen L, Su H. A BERT-based named entity recognition method of warm disease in traditional Chinese medicine. In: 2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA) Ningbo, China: IEEE; 2023: 1226-1231
    Suche in Google Scholar
  • 9 Zhang W, Wu Z, Song G, Huo Q, Wang B. [named entity recognition of traditional Chinese medicine classics based on SiKuBERT and multivariate data embedding]. J South China Univ Technol Nat Sci Ed 2024; 52 (06) 128-137
    Suche in Google Scholar
  • 10 Tuo MM, Yang WZ. Review of entity relation extraction. J Intell Fuzzy Syst 2023; 44 (05) 7391-7405
    Suche in Google Scholar
  • 11 Wang XT, Miao F, Liu HX, Zhang GT, Jin LB. Joint extraction of entities and relations from ancient Chinese medical literature. International Conference on Culture-Oriented Science & Technology (ICCST). 2021: 369-372
    Suche in Google Scholar
  • 12 Jiang S, Li Z, Zhao H, Ding W. Entity-relation extraction as full shallow semantic dependency parsing. IEEE/ACM Trans Audio Speech Lang Process 2024; 32: 1088-1099
    Suche in Google Scholar
  • 13 Zhang T, Huang Z, Wang Y, Wen C, Peng Y, Ye Y. Information extraction from the text data on Traditional Chinese Medicine: a review on tasks, challenges, and methods from 2010 to 2021. Evid Based Complement Alternat Med 2022; 2022: 1679589
    Suche in Google Scholar
  • 14 Liu W, Yin M, Zhang J, Cui L. A joint entity relation extraction model based on relation semantic template automatlyical constructed. Comput Mater Continua 2024; 78 (01) 975-997
    Suche in Google Scholar
  • 15 Hu J, Tang B, Lyu N, He Y, Xiong Y. CMBEE: a constraint-based multi-task learning framework for biomedical event extraction. J Biomed Inform 2024; 150: 104599
    Suche in Google Scholar