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Rui Hua

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Preprint Jul 2026

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

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.

Rui Hua, Zixin Shu, Kai Chang et al. · 0 citations

Open access Jul 2026

TaxEL: Taxonomy-Enhanced Entity Representation Learning for Biomedical Entity Linking.

Biomedical entity linking (BioEL) aims to map textual mentions to standardized concepts in biomedical ontologies. While existing methods have made significant progress, most rely on binary supervision, indiscriminately penalizing all non-gold candidates and overlooking the rich hierarchical structure inherent in biomedical taxonomies. This restricts their ability to capture nuanced semantic relationships and adapt to varying degrees of entity similarity. We propose Taxonomy-Enhanced Entity Linking (TaxEL), a novel framework that unifies taxonomy-guided candidate sampling and structure-aware distributional supervision for BioEL. Specifically, TaxEL introduces: (1) Taxonomy-Guided Contrastive Sampling (TGCS), which systematically integrates both local ontology structure and global semantic similarity to generate informative positive and hard negative samples for each mention; and (2) Structured Semantic Alignment Loss (SSAL), which enforces alignment between model predictions and fine-grained semantic distributions derived from the taxonomy, enabling explicit control over prediction granularity. Experimental results on five public BioEL benchmarks demonstrate that TaxEL achieves state-of-the-art performance in Acc@1, and ablation studies confirm the critical role of both TGCS and SSAL. The TaxEL web service is publicly accessible at http://www.el.tcmkg.com, and all associated data and code can be obtained from https://github.com/TCMAI-BJTU/TaxEL.

Rui Hua, Zeyu Liu, Zixin Shu et al. · IEEE journal of biomedical a... · 0 citations