AutoFAIR : Automatic Data FAIRification via Machine Reading
This addresses the problem of inefficient and limited manual data FAIRification for researchers and data managers, though it is incremental as it builds on existing FAIR principles with automation.
The paper tackles the inefficiency of manual data FAIRification by proposing AutoFAIR, an architecture that automates the process using machine reading and alignment with FAIR principles, resulting in significant improvements in FAIRness scores for data, particularly in mountain hazards.
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.