A Semantic Knowledge Graph Linking Diseases, Patterns, Symptoms, and Herbs for Traditional Chinese Medicine

Version Notice: This knowledge graph has been updated to Version 2.0. This newer version introduces further terminology standardization to better capture nuanced clinical distinctions within Traditional Chinese Medicine, including the disambiguation of overlapping psychiatric, pathogenic-factor, and related clinical concepts. Users are advised to use the latest version for improved semantic granularity and terminology consistency.

Description: This dataset provides the core topological structure of a Traditional Chinese Medicine (TCM) efficacy knowledge graph. Unlike simple efficacy lists, this dataset constructs a full-semantic network integrating the hierarchical logic of "Etiology-Disease-Pattern-Symptom-Efficacy-Herb".

The data is structured as a Property Graph model, containing standardized entities and their semantic relationships, extracted and normalized from authoritative TCM textbooks. It serves as the foundational graph structure for semantic reasoning and efficacy inference.

Dataset Content: The dataset consists of two CSV files representing the graph structure:

Node File (node251228eng.csv): Contains 6,952 entities, including Herbs, Efficacies, Symptoms, Patterns, Diseases, and Etiologies.

Edge File (edge251228eng.csv): Contains 16,619 semantic relationships, defining the logical connections (e.g., has_effect, treated_by, manifests_as) between entities.

Key Features:

Multi-layer Semantics: Covers the complete clinical reasoning chain from pathology to treatment.

Standardized Terminology: Entities are normalized to ensure semantic consistency.

Graph-Ready: Formatted for direct import into graph databases (e.g., Neo4j, Gephi) or network analysis libraries (e.g., NetworkX).

 ContactFor questions, please contact:LI Yuanbai:[e-mail]

This work was supported by:

Key Laboratory of TCM Language and Cognitive Artificial Intelligence,IICTM,CACMS    ZZSYS-1901-CZ,The Study on the Simplification of Medicinal Ingredients in Formulas Based on Efficacy Prediction

Beijing Natural Science Foundation (J230036) – Integrating knowledge graph with the concept of network target to explore and develop innovative Chinese medicine based on aging mechanism in osteoarthritis;

National Key Research and Development Program of China(2023YFC3504005):Development and construction of a real world information platform for Traditional Chinese Medicine Quality.