构建可机器读取的科学知识库，支持精准检索与复用。
Advancing Scientific Knowledge Retrieval and Reuse with a Novel Digital Library for Machine-Readable Knowledge
- 提出ORKG reborn系统，以结构化数据和代码形式表达科学结论与证据
- 支持按统计方法、变量、数据等条件精准检索，提升知识复用效率
- 适合需要高效获取可验证科学结论的研究者，如合成研究与跨学科分析
现有科研数字图书馆（如ACM Digital Library、Semantic Scholar）基于文档中心模型，依赖叙述性文本，难以实现科学知识的机器支持高效复用。本文提出ORKG reborn——一个新兴数字图书馆，通过发布可直接重用的结构化科学知识表达（即‘born-reusable’文章），将科学陈述与其数据和代码支持关联起来。该系统支持基于统计方法、软件包、变量或特定数据约束的精准检索。我们展示了其在计算机科学到土壤科学等多个领域的实际可行性，并证明其相较于主流文献库在信息检索方面的显著潜力。本工作揭示了科学知识数据库的巨大前景及可行构建路径。
原文摘要 · Abstract (English)
Digital libraries for research, such as the ACM Digital Library or Semantic Scholar, do not enable the machine-supported, efficient reuse of scientific knowledge (e.g., in synthesis research). This is because these libraries are based on document-centric models with narrative text knowledge expressions that require manual or semi-automated knowledge extraction, structuring, and organization. We present ORKG reborn, an emerging digital library that supports finding, accessing, and reusing accurate, fine-grained, and reproducible machine-readable expressions of scientific knowledge that relate scientific statements and their supporting evidence in terms of data and code. The rich expressions of scientific knowledge are published as reborn (born-reusable) articles and provide novel possibilities for scientific knowledge retrieval, for instance by statistical methods, software packages, variables, or data matching specific constraints. We describe the proposed system and demonstrate its practical viability and potential for information retrieval in contrast to state-of-the-art digital libraries and document-centric scholarly communication using several published articles in research fields ranging from computer science to soil science. Our work underscores the enormous potential of scientific knowledge databases and a viable approach to their construction.
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