Building evidence-based knowledge graphs from full-text literature for disease-specific biomedical reasoning
This addresses the need for structured, evidence-aware biomedical reasoning for researchers, though it is incremental as it builds on existing LLM and knowledge graph methods.
The authors tackled the problem of unstructured or compressed biomedical evidence by developing EvidenceNet, a framework for building disease-specific knowledge graphs from full-text literature, resulting in datasets like EvidenceNet-HCC with 7,872 evidence records and 49,756 edges, and achieving high technical validation scores such as 98.3% extraction accuracy.
Biomedical knowledge resources often either preserve evidence as unstructured text or compress it into flat triples that omit study design, provenance, and quantitative support. Here we present EvidenceNet, a framework and dataset for building disease-specific knowledge graphs from full-text biomedical literature. EvidenceNet uses a large language model (LLM)-assisted pipeline to extract experimentally grounded findings as structured evidence nodes, normalize biomedical entities, score evidence quality, and connect evidence records through typed semantic relations. We release two resources: EvidenceNet-HCC with 7,872 evidence records, 10,328 graph nodes, and 49,756 edges, and EvidenceNet-CRC with 6,622 records, 8,795 nodes, and 39,361 edges. Technical validation shows high component fidelity, including 98.3% field-level extraction accuracy, 100.0% high-confidence entity-link accuracy, 87.5% fusion integrity, and 90.0% semantic relation-type accuracy. In downstream evaluation, EvidenceNet improves internal and external retrieval-augmented question answering and retains structural signal for future link prediction and target prioritization. These results establish EvidenceNet as a disease-specific resource for evidence-aware biomedical reasoning and hypothesis generation.