FAIRification of Multidimensional and Tabular Data by Instantiating a Core Semantic Model with Domain Knowledge: Case of Meteorology - Archive ouverte HAL Access content directly
Conference Papers Year : 2022

FAIRification of Multidimensional and Tabular Data by Instantiating a Core Semantic Model with Domain Knowledge: Case of Meteorology

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Abstract

Open data is exposed in several formats, including tabular format. However, the meaning of columns, that can also be seen as dimensions, is not always explicit what makes difficult the reuse of this data for data consumers. This paper presents the FAIRification process of tabular and multidimensional datasets that relies on 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. We describe how the instantiation of such a model offers in addition the possibility to describe the semantics of columns using domain ontologies. Once instantiated, this model forms a set of formal metadata that documents the dataset and facilitates understanding by data consumers. This process is then applied to three metereological datasets, for which the degree of improvement of the FAIRness ("I" and "R") has been evaluated.
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Dates and versions

hal-03872638 , version 1 (25-11-2022)

Identifiers

  • HAL Id : hal-03872638 , version 1

Cite

Cassia Trojahn, Mouna Kamel, Amina Annane, Nathalie Aussenac-Gilles, Bao-Long Nguyen, et al.. FAIRification of Multidimensional and Tabular Data by Instantiating a Core Semantic Model with Domain Knowledge: Case of Meteorology. 16th International Conference on Metadata and Semantics Research (MTSR 2022), Nov 2022, London, United Kingdom. à paraître. ⟨hal-03872638⟩
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