AutoFAIR : Automatic Data FAIRification via Machine Reading
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910560830881792 |
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| author | Ma, Tingyan Liu, Wei Lu, Bin Gan, Xiaoying Zhu, Yunqiang Fu, Luoyi Zhou, Chenghu |
| author_facet | Ma, Tingyan Liu, Wei Lu, Bin Gan, Xiaoying Zhu, Yunqiang Fu, Luoyi Zhou, Chenghu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04673 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AutoFAIR : Automatic Data FAIRification via Machine Reading Ma, Tingyan Liu, Wei Lu, Bin Gan, Xiaoying Zhu, Yunqiang Fu, Luoyi Zhou, Chenghu Computation and Language Artificial Intelligence Machine Learning 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. |
| title | AutoFAIR : Automatic Data FAIRification via Machine Reading |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2408.04673 |