LingShu: A Large-Scale Symptom-Centric Contextualized Knowledge Graph Bridging Traditional Chinese Medicine and Modern Biomedicine

• URL: http://arxiv.org/abs/2608.20402v1
• Date: Wed, 29 Jul 2026 00:36:58 GMT
• Title: LingShu: A Large-Scale Symptom-Centric Contextualized Knowledge Graph Bridging Traditional Chinese Medicine and Modern Biomedicine
• Abstract summary: LingShu is a symptom-centric contextualized knowledge graph designed to bridge TCM and modern biomedicine.<n>LingShu integrates multi-source data, including clinical electronic medical records, texts, authoritative TCM texts, through a pipeline combining natural language processing, terminology normalization, and human-in-the-loop verification.
• Score: 24.64642747664969
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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