A Method for Traditional Chinese Medicine Knowledge Graph Dynamic Construction

Xu, Tongyao; Chen, Guo; Du, Lixin; Xu, Jiangying; Zhang, Peng; Feng, Xin; Li, Mingyue

doi:10.1145/3565291.3565323

Cited by 2 publications

(1 citation statement)

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“…研究提供了科学的技术手段 [28] 。同时还有研究团队探索基于脱氧核糖核酸(DNA)甲基化的中医概念量化表征方法，实现证候与现代医学通路的结合创新 [29] 。张鹤译 [40] 等以中医药方剂领域的应用为例，收集领域相关数据并对数据进行预处理，通过强化大语言模型的信息抽取能力，利用生成的自然语言回答，从中抽取出结构化知识，并和专业知识图谱匹配以进行专业验证，同时可以将结构化知识转化成易读的自然语言，实现了大模型与知识图谱的深度结合。Xu [41] 等提出了一种动态增长的知识图构建方法，使用 BERT-CRF 方法进行命名实体识别以及方法进行关系提取，最后将结构化数据合并到现有的知识图谱中，实现动态构建大模型知识库。Ye [42] 等利用知识图谱的检索信息补充中医数…”

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AI-Assisted TCM Syndrome Differentiation: Key Issues and Technical Challenges

Song,

Ma,

Dai

et al. 2024

Strategic Study of CAE

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Traditional Chinese medicine (TCM) syndrome differentiation, as the core of the traditional Chinese medical system, has played an indispensable role in guaranteeing the health of the Chinese nation for thousands of years. Recently, with the collaborative promotion of multiple departments, the TCM technology innovation capability of China has been continuously enhanced. The integration of TCM syndrome differentiation with artificial intelligence AI, big data, and other fields has made new progress. Engineering frontier methods and technologies have provided an effective route for breaking through the theoretical bottlenecks of TCM syndrome differentiation. Against the backdrop of the modernization and intelligent development of TCM diagnosis in the new era, this study summarizes the fundamental theories, basic processes, and key technical links of AI-assisted TCM syndrome differentiation.

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AI-Assisted TCM Syndrome Differentiation: Key Issues and Technical Challenges

Song,

Ma,

Dai

et al. 2024

Strategic Study of CAE

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Traditional Chinese Medicine Knowledge Graph Construction Based on Large Language Models

Zhang,

Hao

2024

Electronics

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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.

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A Method for Traditional Chinese Medicine Knowledge Graph Dynamic Construction

Cited by 2 publications

References 1 publication

AI-Assisted TCM Syndrome Differentiation: Key Issues and Technical Challenges

AI-Assisted TCM Syndrome Differentiation: Key Issues and Technical Challenges

Traditional Chinese Medicine Knowledge Graph Construction Based on Large Language Models

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