Acupuncture and tuina knowledge graph with prompt learning

Xiaoran Li; Xiaosong Han; Siqing Wei; Yanchun Liang; Renchu Guan

doi:10.3389/fdata.2024.1346958

Acupuncture and tuina knowledge graph with prompt learning

Front Big Data. 2024 Apr 8:7:1346958. doi: 10.3389/fdata.2024.1346958. eCollection 2024.

Authors

Xiaoran Li¹, Xiaosong Han¹, Siqing Wei¹, Yanchun Liang^{1

2}, Renchu Guan¹

Affiliations

¹ Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Computer Science and Technology Jilin University, Changchun, China.
² Zhuhai Laboratory of Key Laboratory for Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology of Zhuhai College of Science and Technology, Zhuhai, China.

Abstract

Introduction: Acupuncture and tuina, acknowledged as ancient and highly efficacious therapeutic modalities within the domain of Traditional Chinese Medicine (TCM), have provided pragmatic treatment pathways for numerous patients. To address the problems of ambiguity in the concept of Traditional Chinese Medicine (TCM) acupuncture and tuina treatment protocols, the lack of accurate quantitative assessment of treatment protocols, and the diversity of TCM systems, we have established a map-filling technique for modern literature to achieve personalized medical recommendations.

Methods: (1) Extensive acupuncture and tuina data were collected, analyzed, and processed to establish a concise TCM domain knowledge base. (2)A template-free Chinese text NER joint training method (TemplateFC) was proposed, which enhances the EntLM model with BiLSTM and CRF layers. Appropriate rules were set for ERE. (3) A comprehensive knowledge graph comprising 10,346 entities and 40,919 relationships was constructed based on modern literature.

Results: A robust TCM KG with a wide range of entities and relationships was created. The template-free joint training approach significantly improved NER accuracy, especially in Chinese text, addressing issues related to entity identification and tokenization differences. The KG provided valuable insights into acupuncture and tuina, facilitating efficient information retrieval and personalized treatment recommendations.

Discussion: The integration of KGs in TCM research is essential for advancing diagnostics and interventions. Challenges in NER and ERE were effectively tackled using hybrid approaches and innovative techniques. The comprehensive TCM KG our built contributes to bridging the gap in TCM knowledge and serves as a valuable resource for specialists and non-specialists alike.

Keywords: Entity Relationship Extract; Named Entity Recognition; Traditional Chinese Medicine; knowledge graph; prompt learning.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The National Key Research and Development Program of China No. 2021YFF1201200, the National Natural Science Foundation of China Nos. 62372494 and 62172187, the Science and Technology Planning Project of Jilin Province Nos. 20220201145GX, 20200708112YY, and 20220601112FG, the Science and Technology Planning Project of Guangdong Province No. 2020A0505100018, Guangdong Universities' Innovation Team Project No. 2021 KCXTD015, and Guangdong Key Disciplines Project Nos. 2021ZDJS138 and 2022ZDJS139.