用知识图谱增强大模型，让中医古籍问答更准更懂行。
OpenTCM: A GraphRAG-Empowered LLM-based System for Traditional Chinese Medicine Knowledge Retrieval and Diagnosis
- 构建中医妇科典籍知识图谱，涵盖4.8万实体、15.2万关系。
- 通过图检索生成技术，药材查询与诊断问答得分超现有方法。
- 无需微调模型，适合中医研究者与临床辅助决策使用。
传统中医（TCM）蕴含大量古代医学知识，在现代医疗中仍具重要价值。由于文献内容复杂且范围广泛，引入AI技术对其实现现代化和普及至关重要，但面临古汉语理解难、概念间语义关系复杂等挑战。本文提出OpenTCM系统，结合领域专用中医知识图谱与基于图的检索增强生成（GraphRAG）。首先，从中国医学经典数据库中的68部妇科典籍中提取超过373万字的古文内容，由中医与妇科专家协同验证。其次，利用定制化提示词及中文大模型（如DeepSeek、Kimi），构建包含48,000+实体和152,000+关系的多关系知识图谱，保障语义高保真。最后，通过GraphRAG机制实现无需微调的精准药材知识检索与诊断问答。实验表明，该系统在药材信息检索任务中达到均值专家评分（MES）4.378，诊断问答任务达4.045，在真实中医应用场景中优于当前最优方案。
原文摘要 · Abstract (English)
Traditional Chinese Medicine (TCM) represents a rich repository of ancient medical knowledge that continues to play an important role in modern healthcare. Due to the complexity and breadth of the TCM literature, the integration of AI technologies is critical for its modernization and broader accessibility. However, this integration poses considerable challenges, including the interpretation of obscure classical Chinese texts and the modeling of intricate semantic relationships among TCM concepts. In this paper, we develop OpenTCM, an LLM-based system that combines a domain-specific TCM knowledge graph and Graph-based Retrieval-Augmented Generation (GraphRAG). First, we extract more than 3.73 million classical Chinese characters from 68 gynecological books in the Chinese Medical Classics Database, with the help of TCM and gynecology experts. Second, we construct a comprehensive multi-relational knowledge graph comprising more than 48,000 entities and 152,000 interrelationships, using customized prompts and Chinese-oriented LLMs such as DeepSeek and Kimi to ensure high-fidelity semantic understanding. Last, we empower OpenTCM with GraphRAG, enabling high-fidelity ingredient knowledge retrieval and diagnostic question-answering without model fine-tuning. Experimental evaluations demonstrate that OpenTCM achieves mean expert scores (MES) of 4.378 in ingredient information retrieval and 4.045 in diagnostic question-answering tasks, outperforming state-of-the-art solutions in real-world TCM use cases.
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