发表机构
Beijing Jiaotong University; Hubei Provincial Hospital of Traditional Chinese Medicine; Hubei University of Chinese Medicine; Hubei Province Academy of Traditional Chinese Medicine; China Academy of Chinese Medical Sciences; Xiyuan Hospital; National Clinical Research Center for Chinese Medicine Cardiology; Hubei Provincial Clinical Research Center for Acupuncture and Moxibustion in Obesity Treatment; Hubei Shizhen Laboratory; Tianjin Ta（北京交通大学; 湖北省中医院; 湖北中医药大学; 湖北省中医药研究院; 中国中医科学院; 西苑医院; 国家中医心血管病临床医学研究中心; 湖北省肥胖病针灸临床研究中心; 湖北时珍实验室; 天津（此处原文未完整，按原文提取））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出以症状为中心的LingShu知识图谱，整合多源数据构建混合数据模型，连接中医与现代生物医学，开发集成图可视化等功能的网络平台，为跨医学领域知识关联提供支持。
AI 中文摘要
生物医学知识图谱（KG）是知识组织的核心，但传统二元关系往往难以表征生物医学知识的条件性。症状提供了一个共享的表型层，用于连接中医（TCM）与现代生物医学：中医依靠症状模式进行辨证和选药，而现代生物医学则将临床表现与疾病及分子机制关联起来。本文提出了LingShu，一个以症状为中心的大规模上下文知识图谱，旨在连接中医与现代生物医学。本研究分析的LingShu导出版本包含1733万条原子级实体记录和3947万条关系记录，其中包括1719万条语义三元组和2229万条上下文四元组。LingShu整合了多源数据，包括临床电子病历、权威中医文献、生物医学本体和 curated 知识库，通过结合自然语言处理、术语规范化和人在回路验证的流水线实现。LingShu的一项关键创新是其混合数据模型：它维护64种类型的三元关系模式以确保广泛的连通性，同时纳入35种上下文四元关系模式以捕捉有条件的医学关联。这种双结构方法明确编码了条件知识，为医学关系相关的上下文提供了细粒度表征。这些上下文关系涵盖了证候相关的草药功效、疾病情境下的药物效应、特定人群的临床关联以及机制相关的治疗反应。此外，我们开发了一个网络平台（该http URL），整合了图可视化、基于图的推理和基于证据的知识问答智能体。
英文摘要
Biomedical knowledge graphs (KGs) are pivotal for knowledge organization, yet traditional binary relations often struggle to represent the conditional nature of biomedical knowledge. Symptoms provide a shared phenotypic layer for linking Traditional Chinese Medicine (TCM), which relies on symptom patterns for syndrome differentiation and treatment selection, with modern biomedicine, which connects clinical manifestations to diseases and molecular mechanisms. We present LingShu, a large-scale symptom-centric contextualized knowledge graph designed to bridge TCM and modern biomedicine. The exported version of LingShu analyzed in this study comprises 17.33 million atom-level entity records and 39.47 million relation records, including 17.19 million semantic triples and 22.29 million contextualized quadruples. LingShu integrates multi-source data, including clinical electronic medical records, authoritative TCM texts, biomedical ontologies, and curated knowledge bases, through a pipeline combining natural language processing, terminology normalization, and human-in-the-loop verification. A key innovation of LingShu is its hybrid data model: it maintains 64 typed triple relation patterns to ensure broad connectivity, while incorporating 35 contextual quadruple relation patterns to capture conditional medical associations. This dual-structure approach explicitly encodes conditional knowledge, providing a granular representation of the contexts associated with medical relations. These contextualized relations cover syndrome-dependent herb efficacy, disease-contextualized drug effects, population-specific clinical associations, and mechanism-related therapeutic responses. Furthermore, we developed a web platform (http://www.tcmkg.com/) that integrates graph visualization, graph-based reasoning, and an evidence-grounded knowledge question-answering agent.
