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<p>As one of China’s four major traditional ethnic medical systems, Dai medicine boasts a rich history of over 2 500 years and is an essential component of traditional Chinese medicine and a valuable aspect of Dai culture. However, in the information age, the transmission and development of Dai medicine knowledge face numerous challenges due to the lack of effective integration methods. This study addresses the limitations of existing knowledge graph (KG) construction methods in the area of entity-relation extraction and provides a detailed description of the systematic process for building the Dai medicine KG. This process includes 3 main steps: data preparation, joint extraction of entities and relations, and the storage and visualization of the KG. Additionally, this study also demonstrates the preliminary application of the KG in information retrieval and develops a KG-based question answering system to facilitate knowledge access.</p>

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Bidirectional Entity Relation Extraction Model Enables Effective Knowledge Graph of a Dai Medicine

  • 期刊论文

  • 发表日期：

    2025年10月24日

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  • Lei Ma,
  • Jiawei Wang,
  • Yukun Yan,
  • Jiangkai Yan,
  • Jingtao Li,
  • Debiao Du,
  • Dangguo Shao

摘要

As one of China’s four major traditional ethnic medical systems, Dai medicine boasts a rich history of over 2 500 years and is an essential component of traditional Chinese medicine and a valuable aspect of Dai culture. However, in the information age, the transmission and development of Dai medicine knowledge face numerous challenges due to the lack of effective integration methods. This study addresses the limitations of existing knowledge graph (KG) construction methods in the area of entity-relation extraction and provides a detailed description of the systematic process for building the Dai medicine KG. This process includes 3 main steps: data preparation, joint extraction of entities and relations, and the storage and visualization of the KG. Additionally, this study also demonstrates the preliminary application of the KG in information retrieval and develops a KG-based question answering system to facilitate knowledge access.