arXiv AI
NLP-Driven Knowledge Extraction and Thematic Classification of Translated Ancient Indian Medical Texts
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arXiv AI
Aug 24
LingShu: A Large-Scale Symptom-Centric Contextualized Knowledge Graph Bridging Traditional Chinese Medicine and Modern Biomedicine
LingShu is a large-scale, symptom‑centric knowledge graph that bridges Traditional Chinese Medicine (TCM) and modern biomedicine. It contains 17.33 million entity records and 39.47 million relation records, combining 17.19 million semantic triples with 22.29 million contextualized quadruples to encode conditional medical associations. The graph integrates data from electronic medical records, TCM texts, biomedical ontologies, and curated knowledge bases, and is supported by a web platform offering visualization, reasoning, and evidence‑grounded question answering.
arXiv AI
2d ago
MiNER: Fine-Tuned Biomedical Natural Language Processing for Malaria Disease Entity Recognition in Clinical Texts
arXiv:2609.00073v1 Announce Type: new Abstract: Malaria remains a significant global health burden, necessitating continuous research efforts to understand its complex molecular mechanisms, epidemiol...
arXiv AI
Aug 21
Automatic bioinformatic software named entity recognition from literature
arXiv:2608. 19201v1 Announce Type: cross Abstract: Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale.
arXiv AI
Aug 12
Language corpora for the Dutch medical domain
arXiv:2604. 25374v2 Announce Type: replace-cross Abstract: Background: Dutch medical corpora are scarce, limiting NLP development.
arXiv AI
1d ago
Unifying biomedical knowledge in a modern multimodal graph
OptimusKG is a multimodal biomedical labeled property graph that integrates structured and semi‑structured resources to preserve detailed, type‑specific metadata across molecular, anatomical, clinical, and environmental domains. The graph contains nearly 191,000 nodes, over 21.8 million edges, and more than 67 million property instances derived from 18 ontologies, with a top‑level schema that enforces node and edge constraints while retaining granular provenance. Validation using the PaperQA3 agent found that 70.0% of sampled edges are supported by literature evidence, and the graph offers a standardized resource for machine learning, knowledge‑grounded retrieval, and hypothesis generation in biomedical research.
arXiv Machine Learning
Aug 6
Neighborhood-Aware Dual Biomedical Entity Linking
arXiv:2608. 04144v1 Announce Type: cross Abstract: Biomedical entity linking grounds mentions in clinical and scientific text to entities in a curated knowledge base (KB) with ontological structure, which supports downstream applications such as literature-scale information extraction and patient-record normalization.