WeKG-MF: a Knowledge Graph of Observational Weather Data

Nadia Yacoubi Ayadi (1, 2, 3) , Catherine Faron (1, 2, 3) , Franck Michel (2, 3, 4) , Fabien Gandon (5, 2) , Olivier Corby (5, 2)

1 UniCA - Université Côte d'Azur
2 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
3 I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis
4 CNRS - Centre National de la Recherche Scientifique
5 CRISAM - Inria Sophia Antipolis - Méditerranée

In this paper, we present the WeKG-MF Knowledge Graph constructed from open weather observations published by Météo-France institution. WeKG-MF relies on a semantic model that formalizes knowledge about meteorological observational data. The model is generic enough to be adopted and extended by meteorological data providers to publish and integrate their sources while complying with Linked Data principles. WeKG-MF offers access to a large number of meteorological variables described through spatial and temporal dimensions and thus has the potential to serve several scientific case studies from different domains including agriculture, agronomy, environment, climate change and natural disasters.

Mots clés: Knowledge Graph; Semantic Modelling; Observational Data; Linked Data; Meteorology

Domaines: Informatique [cs]; Web

Nadia Yacoubi Ayadi, Catherine Faron, Franck Michel, Fabien Gandon, Olivier Corby. WeKG-MF: a Knowledge Graph of Observational Weather Data. The Semantic Web: ESWC 2022 Satellite Events, May 2022, Hersonissos, Greece. ⟨hal-03657694v1⟩