用于阿尔茨海默症中药研发的知识图谱数据集

1.知识抽取阶段：在阿尔茨海默症中医知识图谱构建过程中，知识抽取阶段采用深度学习方法系统化处理多源异构数据。运用大语言模型对中医古籍进行语义解析，精准提取"病-证-症"诊断三元组实体关系。
2.知识融合处理流程：面对中医药领域多源数据存在的术语差异和标准不统一问题，建立了严格的知识融合体系。首先依据ICD11国际标准构建中医术语映射词典，对来自古籍、方剂库和现代文献的12万余条专业术语,通过方剂，证候，中药材等相关的国标进行标准化对齐。针对不同来源方剂数据的配伍冲突，开发了基于置信度加权的冲突消解算法，建立多维度的可信度评估模型，有效解决不同典籍间方剂组成差异问题。
经上述算法规则处理的数据集，能为中医药研究阿尔茨海默症中药研发的提供精准的知识数据，涵盖中成药、方剂、功效、证候和症状等知识数据。

1. Knowledge Extraction Stage: During the construction of the Traditional Chinese Medicine (TCM) knowledge graph for Alzheimer's disease, deep learning methods are adopted to systematically process multi-source heterogeneous data in the knowledge extraction stage. Large Language Models (LLMs) are utilized to conduct semantic parsing on ancient TCM books, so as to accurately extract the "disease-syndrome-symptom" diagnostic triplet entity relationships.
2. Knowledge Fusion Processing Workflow: Aiming at the issues of terminology differences and inconsistent standards in multi-source data of the TCM field, a strict knowledge fusion system has been established. Firstly, a TCM terminology mapping dictionary is constructed based on the international standard ICD-11. More than 120,000 professional terms from ancient books, prescription databases and modern literature are standardized and aligned via national standards related to prescriptions, syndromes, Chinese medicinal materials and other relevant categories. For the compatibility conflicts of prescription data from different sources, a confidence-weighted conflict resolution algorithm has been developed, and a multi-dimensional credibility evaluation model has been established, which effectively solves the problem of differences in prescription composition among different classics.
The dataset processed by the aforementioned algorithmic rules can provide accurate knowledge data for TCM research and the development of traditional Chinese medicines targeting Alzheimer's disease, covering knowledge contents such as proprietary Chinese medicines, prescriptions, efficacies, syndromes and symptoms.

搜集汇总

背景与挑战

背景概述

该数据集是一个针对阿尔茨海默症中药研发的知识图谱，包含633万条结构化数据，涵盖证候、方剂、基因等18个字段，每年更新。它适用于中药企业、科研机构等进行新药发现、机理研究和临床辅助诊断，通过深度学习算法整合多源数据，解决中医药研发中的机理不清晰和效率问题。