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.
Abstract (translated)
生物医学知识资源通常要么以非结构化文本形式保留证据，要么将其压缩为省略了研究设计、来源和定量支持的扁平三元组。在此，我们提出EvidenceNet，一个用于从生物医学全文文献中构建疾病特定知识图谱的框架和数据集。EvidenceNet采用大语言模型辅助的流程，将实验性发现提取为结构化证据节点，规范化生物医学实体，评估证据质量，并通过类型化语义关系连接证据记录。我们发布了两项资源：包含7,872条证据记录、10,328个图谱节点和49,756条边的EvidenceNet-HCC，以及包含6,622条记录、8,795个节点和39,361条边的EvidenceNet-CRC。技术验证显示高组件保真度，包括98.3%的字段级提取准确率、100.0%的高置信度实体链接准确率、87.5%的融合完整性以及90.0%的语义关系类型准确率。在下游评估中，EvidenceNet提升了内部和外部检索增强问答的表现，并为未来链接预测和靶点优先级排序保留了结构信号。这些结果确立了EvidenceNet作为面向证据感知的生物医学推理与假设生成的疾病特定资源。
URL
https://arxiv.org/abs/2603.28325
