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Conference Papers Year : 2022

A FAIR Core Semantic Metadata Model for FAIR Multidimensional Tabular Datasets

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Abstract

Tabular format is a common format in open data. However, the meaning of columns is not always explicit which makes if difficult for non-domain experts to reuse the data. While most efforts in making data FAIR are limited to semantic metadata describing the overall features of datasets, such a description is not enough to ensure data interoperability and reusability. This paper proposes to reduce this weakness thanks to a (FAIR) core semantic model that is able to represent different kinds of metadata, including the data schema and the internal structure of a dataset. This model can then be linked to domain-specific definitions to provide domain understanding to data consumers.
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Dates and versions

hal-03872685 , version 1 (25-11-2022)

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Cite

Cassia Trojahn, Mouna Kamel, Amina Annane, Nathalie Aussenac-Gilles, Bao Long Nguyen. A FAIR Core Semantic Metadata Model for FAIR Multidimensional Tabular Datasets. 23rd International Conference on Knowledge Engineering and Knowledge Management (EKAW 2022), Sep 2022, Bolzano, Italy. pp.174 - 181, ⟨10.1007/978-3-031-17105-5_13⟩. ⟨hal-03872685⟩
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