<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study centers on the English translation of Chinese medicine terms and the construction of knowledge graph, to solve the problem of the accuracy of Chinese medicine terminology translation through scientific and technological means, and to improve the communication efficiency and accuracy of Chinese medicine in the international arena. First, we collected Chinese medicine-related data from websites such as “Seek Medical Help” and “Seek Medical Help” through crawler technology, then performed data cleaning and fusion processing to eliminate data redundancy and inconsistency. Then, we defined 17 types of concepts and 22 conceptual relationships related to TCM ancient books, as well as the corresponding attribute definitions, to ensure the accuracy and completeness of the knowledge graph. The Bi-LSTM model for text disambiguation and Labeling further improved data processing efficiency. Eventually, this study successfully constructed a knowledge graph of English translation of TCM terms containing 50051 nodes and 13521 relations. This knowledge graph improves the accuracy of TCM terminology translation and provides a powerful tool for international dissemination and academic research of TCM terminology.</p>
</abstract>

This study centers on the English translation of Chinese medicine terms and the construction of knowledge graph, to solve the problem of the accuracy of Chinese medicine terminology translation through scientific and technological means, and to improve the communication efficiency and accuracy of Chinese medicine in the international arena. First, we collected Chinese medicine-related data from websites such as “Seek Medical Help” and “Seek Medical Help” through crawler technology, then performed data cleaning and fusion processing to eliminate data redundancy and inconsistency. Then, we defined 17 types of concepts and 22 conceptual relationships related to TCM ancient books, as well as the corresponding attribute definitions, to ensure the accuracy and completeness of the knowledge graph. The Bi-LSTM model for text disambiguation and Labeling further improved data processing efficiency. Eventually, this study successfully constructed a knowledge graph of English translation of TCM terms containing 50051 nodes and 13521 relations. This knowledge graph improves the accuracy of TCM terminology translation and provides a powerful tool for international dissemination and academic research of TCM terminology.

Construction of a Knowledge Graph Based on the Study of English Translation of Traditional Chinese Medicine Terminology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This study centers on the English translation of Chinese medicine terms and the construction of knowledge graph, to solve the problem of the accuracy of...

Construction of a Knowledge Graph Based on the Study of English Translation of Traditional Chinese Medicine Terminology

Publié en ligne: 26 févr. 2024

Pages: -

Reçu: 12 janv. 2024

Accepté: 22 janv. 2024

DOI: https://doi.org/10.2478/amns-2024-0564

Mots clés
English translation of Chinese medicine terms, Knowledge graph, Data cleaning, Attribute definition

© 2024 Lingzhen Zhao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Construction of a Knowledge Graph Based on the Study of English Translation of Traditional Chinese Medicine Terminology

Publié en ligne: 26 févr. 2024

Pages: -

Reçu: 12 janv. 2024

Accepté: 22 janv. 2024

DOI: https://doi.org/10.2478/amns-2024-0564

Mots clésEnglish translation of Chinese medicine terms, Knowledge graph, Data cleaning, Attribute definition

© 2024 Lingzhen Zhao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
English translation of Chinese medicine terms, Knowledge graph, Data cleaning, Attribute definition