构建600万条科学贡献图谱，助力自动化科技路线图绘制
The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale
- 从65.5万篇论文中提取科学贡献并建立前后依赖关系
- 构建包含3600万条前提边的贡献图谱，支持技术演进分析
- 可辅助科研评估与自动发现，适合科研管理与战略规划者
科学发现很少孤立产生，而是基于先前成果。我们提出自动化科技路线图任务：从学术论文中提取科学贡献，并将其与前置技术关联。本文构建了科学贡献图谱（Scientific Contribution Graph），涵盖655,000篇开放获取论文中的600万条详细科学贡献，以及3600万条前提依赖边，覆盖计算机科学、医学、生物学、物理、化学等多个领域。我们进一步提出科学前提预测任务，即模型预测哪些现有技术能促成未来发现，结果表明当前模型在时间过滤的回测中已达到0.48 MAP。我们预期此类科技路线图资源将有助于科学影响力评估与自动化科学发现。
原文摘要 · Abstract (English)
Scientific contributions rarely develop in isolation, but instead build upon prior discoveries. We formulate the task of automated technological roadmapping as extracting scientific contributions from scholarly articles and linking them to their prerequisites. We present the Scientific Contribution Graph, a large-scale resource containing 6 million detailed scientific contributions extracted from 655k open-access papers spanning computer science, medicine, biology, physics, chemistry, and other sciences, and connected by 36 million prerequisite edges. We further introduce scientific prerequisite prediction, a scientific discovery task in which models predict which existing technologies can enable future discoveries, and show that contemporary models are rapidly improving on this task, reaching 0.48 MAP when evaluated using temporally-filtered backtesting. We anticipate technological roadmapping resources such as this will support scientific impact assessment and automated scientific discovery.
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