Traditional Chinese Medicine Knowledge Graph Construction Based on Large Language Models

• Published in: Electronics (Basel) Vol. 13; no. 7; p. 1395
• Main Authors: Zhang, Yichong, Hao, Yongtao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2024
• Subjects: Accuracy; Algorithms; Automation; Computational linguistics; Deep learning; Efficiency; Graphs; Knowledge management; Knowledge representation; Language processing; Large language models; Machine learning; Medicine, Chinese; Modernization; Natural language interfaces; Neural networks; Ontology; Search engines; Semantics; Subject specialists; Traditional Chinese medicine; Workloads; China
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics13071395

This study explores the use of large language models in constructing a knowledge graph for Traditional Chinese Medicine (TCM) to improve the representation, storage, and application of TCM knowledge. The knowledge graph, based on a graph structure, effectively organizes entities, attributes, and relationships within the TCM domain. By leveraging large language models, we collected and embedded substantial TCM–related data, generating precise representations transformed into a knowledge graph format. Experimental evaluations confirmed the accuracy and effectiveness of the constructed graph, extracting various entities and their relationships, providing a solid foundation for TCM learning, research, and application. The knowledge graph has significant potential in TCM, aiding in teaching, disease diagnosis, treatment decisions, and contributing to TCM modernization. In conclusion, this paper utilizes large language models to construct a knowledge graph for TCM, offering a vital foundation for knowledge representation and application in the field, with potential for future expansion and refinement.