AutoFAIR：通过机器阅读实现数据 FAIR 化自动化
AutoFAIR : Automatic Data FAIRification via Machine Reading
AI总结：
本文提出 AutoFAIR 架构，通过操作对齐、Web Reader 元数据抽取及本体引导的语义匹配，自动提升数据的 FAIR 性，并在山地灾害等数据上验证其效果。
摘要：
数据的爆炸式增长推动了数据驱动研究，促进了多个领域的进步。FAIR 原则作为一项指导性标准出现，旨在提升数据的可发现性、可访问性、互操作性和可重用性。然而，当前工作主要集中于人工数据 FAIR 化，只能处理目标数据且效率不足。为解决这一问题，我们提出 AutoFAIR，一种旨在自动提升数据 FAIR 性的架构。首先，我们将每项数据和元数据操作与特定 FAIR 指标对齐，以指导机器可执行的动作。随后，我们利用 Web Reader 基于语言模型自动提取元数据，即使在缺少结构化数据网页模式的情况下也能完成。接着，采用 FAIR Alignment，通过本体引导和语义匹配使元数据符合 FAIR 原则。最后，将 AutoFAIR 应用于各类数据，尤其是山地灾害领域的数据，我们观察到数据的可发现性、可访问性、互操作性和可重用性均显著提升。应用 AutoFAIR 前后的 FAIR 性评分表明数据价值得到增强。
英文摘要：
The explosive growth of data fuels data-driven research, facilitating progress across diverse domains. The FAIR principles emerge as a guiding standard, aiming to enhance the findability, accessibility, interoperability, and reusability of data. However, current efforts primarily focus on manual data FAIRification, which can only handle targeted data and lack efficiency. To address this issue, we propose AutoFAIR, an architecture designed to enhance data FAIRness automately. Firstly, We align each data and metadata operation with specific FAIR indicators to guide machine-executable actions. Then, We utilize Web Reader to automatically extract metadata based on language models, even in the absence of structured data webpage schemas. Subsequently, FAIR Alignment is employed to make metadata comply with FAIR principles by ontology guidance and semantic matching. Finally, by applying AutoFAIR to various data, especially in the field of mountain hazards, we observe significant improvements in findability, accessibility, interoperability, and reusability of data. The FAIRness scores before and after applying AutoFAIR indicate enhanced data value.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
