Building evidence-based knowledge graphs from full-text literature for disease-specific biomedical reasoning

2026-03-30

AI Summary

Existing biomedical knowledge resources are often limited to unstructured text or flat triples that omit critical contextual details such as study design, source provenance, and quantitative support, thereby hindering fine-grained reasoning. This work proposes a large language model–assisted end-to-end framework that extracts experimental findings from full-text literature as structured evidence nodes, standardizes biomedical entities, evaluates evidence quality, and constructs disease-specific knowledge graphs via typed semantic relations. To our knowledge, this is the first approach to systematically build structured evidence graphs from full-text articles while preserving study design, source attribution, and quantitative backing. The authors release two high-quality datasets, EvidenceNet-HCC and EvidenceNet-CRC, with component-level validation accuracy of 98.3%, demonstrating significant performance gains in retrieval-augmented question answering, link prediction, and target prioritization tasks.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Natural Language Processing: Information Extraction

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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.

Problem

• knowledge graph
• evidence representation
• biomedical reasoning
• structured evidence
• disease-specific

Innovation

• EvidenceNet
• knowledge graph
• large language model
• evidence extraction
• biomedical reasoning