Structured Dataset of Traditional Efficacies for Chinese Herbal Medicines

Description: This dataset provides a structured and computable resource of traditional therapeutic effects (efficacies) for Chinese herbal medicines. Its main purpose is to transform classical TCM efficacy descriptions from natural-language expressions into standardized herb-efficacy relations that can be directly used for knowledge graph construction, formula efficacy prediction, herb-symptom association mining, and AI-based reasoning over traditional therapeutic knowledge.

The dataset links Chinese herbs to explicit and rule-expanded efficacy nodes. Explicit efficacy nodes are directly structured from authoritative TCM textual sources, while expanded efficacy nodes are inferred through rule-based semantic reasoning over a TCM knowledge graph. In this way, the dataset not only records what therapeutic effects are explicitly stated for each herb, but also provides computable extensions of those effects to related symptoms, patterns, sub-patterns, and pathogenesis-related efficacy concepts.

Version 2.0 Release Notice: This is Version 2.0 of the Structured Dataset of Traditional Efficacies for Chinese Herbal Medicines. This release reorganizes the previous single-table dataset into separate herb-efficacy relation files and rule-specific evidence files, improving transparency, traceability, and downstream computability. A new field, Efficacy_Node_Type, is included to distinguish explicit efficacy nodes from rule-based expanded nodes generated by Rule A, Rule B, Rule C, and Rule D.

Upstream Knowledge Graph Dependency Note: The rule-based expanded efficacy relations in this dataset were generated based on the updated Version 2.0 of the related TCM knowledge graph. Because the upstream knowledge graph was revised and further standardized, the semantic reasoning results produced by Rules A-D were also recalculated and updated accordingly. Therefore, the relation counts and expanded efficacy results in this version may differ from those in the previous release. The related knowledge graph is available at: https://doi.org/10.5281/zenodo.20061549

Dataset Content: The dataset consists of five herb-efficacy relation files and four rule-specific evidence files.

Herb-Efficacy Relation Files:

1. explicit_herb_efficacy_relations_v2.csv: Directly curated herb-to-efficacy relations for explicit efficacy nodes.

2. expanded_herb_efficacy_relations_ruleA_v2.csv: Herb-to-efficacy relations expanded through Rule A.

3. expanded_herb_efficacy_relations_ruleB_v2.csv: Herb-to-efficacy relations expanded through Rule B.

4. expanded_herb_efficacy_relations_ruleC_v2.csv: Herb-to-efficacy relations expanded through Rule C.

5. expanded_herb_efficacy_relations_ruleD_v2.csv: Herb-to-efficacy relations expanded through Rule D.

Evidence Files:

1. herb_efficacy_expansion_evidence_ruleA_v2.csv: Rule A evidence table documenting the reasoning paths for Rule A expansions.

2. herb_efficacy_expansion_evidence_ruleB_v2.csv: Rule B evidence table documenting the reasoning paths for Rule B expansions.

3. herb_efficacy_expansion_evidence_ruleC_v2.csv: Rule C evidence table documenting the reasoning paths for Rule C expansions.

4. herb_efficacy_expansion_evidence_ruleD_v2.csv: Rule D evidence table documenting the reasoning paths for Rule D expansions.

Key Statistics: The five herb-efficacy relation files contain 34,133 relation records in total, covering 483 unique herbs and 2,564 unique target efficacy nodes. After deduplication by source_id and target_id, the dataset contains 33,859 unique herb-efficacy pairs. The four rule-specific evidence files contain 27,876 evidence records in total.

Data Structure: The relation files are provided in CSV format with UTF-8 encoding. The shared fields include source_id, source_ENGLISHNAME, source_LABEL, target_id, target_ENGLISHNAME, target_LABEL, TYPE, and Efficacy_Node_Type. The TYPE field represents the directed has_effect relation between herbs and efficacy nodes. The Efficacy_Node_Type field indicates whether the entry belongs to explicit efficacy nodes or rule-based expanded nodes.

The evidence files provide rule-specific provenance information for expanded efficacy relations. Because Rules A-D involve different semantic reasoning paths, the evidence files contain different intermediate-node fields. The number of evidence records may differ from the number of final expanded herb-efficacy relations because a single herb-efficacy relation may be supported by multiple reasoning paths or intermediate evidence nodes.

Potential Applications: This resource is intended for researchers in TCM informatics, biomedical knowledge graphs, and AI-based prescription analysis. It supports formula efficacy prediction, herb-efficacy relation mining, symptom-herb association analysis, knowledge graph enrichment, semantic reasoning, and computational analysis of traditional therapeutic knowledge.

Related Publication:

Yuanbai L, Fangzhou L, Yihao L, Yu D, Meng L, Qin Q, Yang Y, Hongming M. A Knowledge Graph-Driven Hypergeometric Efficacy Prediction Model for Classical Traditional Chinese Herbal Formulas. Methods Inf Med. 2026 Apr 7. doi: 10.1055/a-2841-4549. Epub ahead of print. PMID: 41895302.

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

数据集说明：本数据集为中药材提供了结构化且可计算的传统治疗效应（疗效，efficacy）资源。其核心目标是将古典中医药（Traditional Chinese Medicine, TCM）疗效的自然语言描述，转化为标准化的药材-疗效关联关系，可直接用于知识图谱构建、方剂疗效预测、药材-症状关联挖掘以及基于人工智能（Artificial Intelligence, AI）的传统治疗知识推理。
本数据集将中药材与显式疗效节点及规则扩展疗效节点相关联。其中，显式疗效节点直接从权威中医药文本来源结构化提取，而扩展疗效节点则通过基于规则的语义推理，在中医药知识图谱上推导得出。借此，本数据集不仅记录了每味药材明确记载的治疗效应，还将这些疗效扩展至相关症状、证型、亚型及与病机相关的疗效概念，实现可计算化延伸。

2.0版本发布说明：本数据集为《中药材传统疗效结构化数据集》2.0版本。本次发布将此前的单表数据集重构为独立的药材-疗效关联文件与规则专属证据文件，提升了数据集的透明度、可追溯性与下游可计算性。新增字段`Efficacy_Node_Type`，用于区分显式疗效节点与由规则A（Rule A）、规则B（Rule B）、规则C（Rule C）及规则D（Rule D）生成的规则扩展疗效节点。

上游知识图谱依赖说明：本数据集内的规则扩展疗效关联关系，均基于更新至2.0版本的相关中医药知识图谱生成。由于上游知识图谱已完成修订与进一步标准化，规则A至规则D生成的语义推理结果也已同步重新计算并更新。因此，本版本的关联记录数与扩展疗效结果可能与前一版本存在差异。相关知识图谱可通过以下链接获取：https://doi.org/10.5281/zenodo.20061549

数据集内容：本数据集包含5份药材-疗效关联文件与4份规则专属证据文件。

药材-疗效关联文件：
1. explicit_herb_efficacy_relations_v2.csv：直接整理得到的显式疗效节点对应的药材-疗效关联关系。
2. expanded_herb_efficacy_relations_ruleA_v2.csv：通过规则A扩展得到的药材-疗效关联关系。
3. expanded_herb_efficacy_relations_ruleB_v2.csv：通过规则B扩展得到的药材-疗效关联关系。
4. expanded_herb_efficacy_relations_ruleC_v2.csv：通过规则C扩展得到的药材-疗效关联关系。
5. expanded_herb_efficacy_relations_ruleD_v2.csv：通过规则D扩展得到的药材-疗效关联关系。

证据文件：
1. herb_efficacy_expansion_evidence_ruleA_v2.csv：规则A证据表，记录规则A扩展的推理路径。
2. herb_efficacy_expansion_evidence_ruleB_v2.csv：规则B证据表，记录规则B扩展的推理路径。
3. herb_efficacy_expansion_evidence_ruleC_v2.csv：规则C证据表，记录规则C扩展的推理路径。
4. herb_efficacy_expansion_evidence_ruleD_v2.csv：规则D证据表，记录规则D扩展的推理路径。

核心统计数据：5份药材-疗效关联文件总计包含34133条关联记录，覆盖483种独特中药材与2564个独特目标疗效节点。经以`source_id`与`target_id`为依据去重后，本数据集共包含33859条唯一药材-疗效配对。4份规则专属证据文件总计包含27876条证据记录。

数据结构：关联文件采用UTF-8编码的CSV格式存储。共享字段包括`source_id`、`source_ENGLISHNAME`、`source_LABEL`、`target_id`、`target_ENGLISHNAME`、`target_LABEL`、`TYPE`及`Efficacy_Node_Type`。其中`TYPE`字段表示药材与疗效节点之间的有向“具有疗效”（has_effect）关联关系；`Efficacy_Node_Type`字段用于标识该条目属于显式疗效节点还是规则扩展疗效节点。
证据文件为扩展疗效关联关系提供规则专属的溯源信息。由于规则A至规则D采用不同的语义推理路径，各证据文件包含不同的中间节点字段。由于单条药材-疗效关联关系可能对应多条推理路径或中间证据节点，因此证据记录数与最终扩展后的药材-疗效关联记录数可能存在差异。

潜在应用场景：本数据集面向中医药信息学、生物医学知识图谱及基于人工智能的方剂分析领域的研究人员。其可支持方剂疗效预测、药材-疗效关联挖掘、症状-药材关联分析、知识图谱扩充、语义推理及传统治疗知识的计算分析。

相关发表文献：
Yuanbai L, Fangzhou L, Yihao L, Yu D, Meng L, Qin Q, Yang Y, Hongming M. 面向经典中药方剂的知识图谱驱动超几何疗效预测模型[J]. 医学信息学方法（Methods Inf Med）. 2026 Apr 7. doi: 10.1055/a-2841-4549. 预印本已在线发表. PMID: 41895302.

联系方式：如有疑问，请联系：LI Yuanbai：[e-mail]