AutoFAIR : Automatic Data FAIRification via Machine Reading

• URL: http://arxiv.org/abs/2408.04673v1
• Date: Wed, 7 Aug 2024 17:36:58 GMT
• Title: AutoFAIR : Automatic Data FAIRification via Machine Reading
• Authors: Tingyan Ma, Wei Liu, Bin Lu, Xiaoying Gan, Yunqiang Zhu, Luoyi Fu, Chenghu Zhou,
• Abstract summary: We propose AutoFAIR, an architecture designed to enhance data FAIRness automately. We align each data and metadata operation with specific FAIR indicators to guide machine-executable actions. We observe significant improvements in findability, accessibility, interoperability, and reusability of data.
• Score: 28.683653852643015
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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