为可解释推理进行生物医学知识图表检索的缩放:应用于临床诊断预测的应用
medRxiv : the preprint server for health sciences
|February 6, 2026
概括
在大型生物医学知识图 (KG) 上,LogosKG提供高效的多跳转检索. 这种硬件优化的系统通过实现可解释的,深度的图形穿越来改善诊断推理.
科学领域:
- 生物医学信息学 生物医学信息学
- 图形数据库 图形数据库
- 计算生物学 计算生物学
背景情况:
- 生物医学知识图 (KG) 对于组织复杂的生物和临床数据至关重要.
- 从大型KG中进行可扩展和可解释的多跳检索是一个重要的计算挑战.
- 目前的系统在与先进的生物医学推理所需的规模和深度作斗争.
研究的目的:
- 开发一个硬件优化的系统,LogosKG,在大型生物医学KG上进行高效的k-hop检索.
- 为了解决访问深度,多跳图结构的计算瓶.
- 通过改进KG穿越来增强诊断推理.
主要方法:
- 逻辑KG使用符号图表配方和硬件高效执行 k-hop 穿越.
- 关键技术包括程度意识的分区,跨分区路由和按需缓存.
- 该系统旨在扩展到数十亿边缘图形,同时保持检索准确度.
主要成果:
- 与基于CPU和GPU的方法相比,LogosKG显示了相当大的效率提升.
- 该系统成功地扩展到非常大的生物医学图 (数十亿边缘尺度).
- 实验表明,检索保真度和性能得到了改善.
结论:
- 洛格斯KG提供了一个可扩展和高效的解决方案,用于深度生物医学KG的多跳转检索.
- 通过LogosKG访问深度图形结构可以促进可解释的诊断证据的传播.
- 评估表明使用高跳式KG检索提高了诊断推理质量 (准确性,可理解性,简洁性).
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