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Visualizing Motion Patterns in Acupuncture Manipulation
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Acupuncture and tuina knowledge graph with prompt learning.

Xiaoran Li1, Xiaosong Han1, Siqing Wei1

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

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|April 23, 2024
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Summary
This summary is machine-generated.

A new knowledge graph (KG) for Traditional Chinese Medicine (TCM) improves understanding of acupuncture and tuina treatments. This structured data facilitates personalized medical recommendations and advances TCM research.

Keywords:
Entity Relationship ExtractNamed Entity RecognitionTraditional Chinese Medicineknowledge graphprompt learning

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Area of Science:

  • Computational linguistics
  • Knowledge representation
  • Traditional Chinese Medicine (TCM)

Background:

  • Acupuncture and tuina are established TCM therapies.
  • Existing TCM treatment protocols lack standardization and quantitative assessment.
  • Diverse TCM systems present challenges for consistent application.

Purpose of the Study:

  • To develop a structured knowledge base for TCM acupuncture and tuina.
  • To enable personalized medical recommendations through data analysis.
  • To address ambiguity and improve quantitative assessment of TCM protocols.

Main Methods:

  • Collected and processed extensive acupuncture and tuina data.
  • Developed a template-free Chinese Named Entity Recognition (NER) joint training method (TemplateFC) enhancing the EntLM model with BiLSTM and CRF layers.
  • Constructed a comprehensive TCM knowledge graph (KG) with 10,346 entities and 40,919 relationships.

Main Results:

  • Created a robust TCM KG with diverse entities and relationships.
  • TemplateFC significantly improved Chinese NER accuracy, overcoming entity identification and tokenization issues.
  • The KG offers insights into acupuncture and tuina, supporting efficient information retrieval and personalized treatment recommendations.

Conclusions:

  • Integrating KGs is crucial for advancing TCM diagnostics and interventions.
  • Innovative techniques effectively addressed NER and relation extraction challenges.
  • The developed TCM KG bridges knowledge gaps and serves as a valuable resource for TCM research and practice.