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.

翻译：生物医学知识资源通常要么将证据保留为非结构化文本，要么将其压缩为忽略研究设计、来源和定量支撑的扁平三元组。本文提出了EvidenceNet——一种从全文生物医学文献中构建疾病特定知识图谱的框架与数据集。EvidenceNet采用大语言模型（LLM）辅助流水线，将基于实验的发现提取为结构化证据节点，对生物医学实体进行标准化处理，评估证据质量，并通过带类型的语义关系连接证据记录。我们发布了两个资源：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。技术验证显示各组件具有高保真度，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提升了内部与外部检索增强问答的性能，并保留了可供未来链路预测与目标优先级排序使用的结构信号。这些结果确立了EvidenceNet作为疾病特异性资源在证据感知生物医学推理与假设生成中的价值。