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A FAIR Core Semantic Metadata Model for FAIR Multidimensional Tabular Datasets

Part of the Lecture Notes in Computer Science book series (LNAI,volume 13514)


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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This work is funded by the ANR (French National Research Agency) Semantics4FAIR project, contract ANR-19-DATA-0014-01.

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Correspondence to Cassia Trojahn .

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Trojahn, C., Kamel, M., Annane, A., Aussenac-Gilles, N., Nguyen, B.L. (2022). A FAIR Core Semantic Metadata Model for FAIR Multidimensional Tabular Datasets. In: Corcho, O., Hollink, L., Kutz, O., Troquard, N., Ekaputra, F.J. (eds) Knowledge Engineering and Knowledge Management. EKAW 2022. Lecture Notes in Computer Science(), vol 13514. Springer, Cham.

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  • Print ISBN: 978-3-031-17104-8

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