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A Model for Meteorological Knowledge Graphs: Application to Météo-France Data


To study and predict meteorological phenomenons and to include them in broader studies, the ability to represent and exchange meteorological data is of paramount importance. A typical approach in integrating and publishing such data now is to formalize a knowledge graph relying on Linked Data and semantic Web standard models and practices.In this paper, we first discuss the semantic modelling issues related to spatio-temporal data such as meteorological observational data. We motivate the reuse of a network of existing ontologies to define a semantic model in which meteorological parameters are semantically defined, described and integrated. 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.Finally, we present a meteorological knowledge graph of weather observations based on our proposed model, published in the form of an RDF dataset, that we produced by transforming observation records made by Météo-France weather stations.It covers 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.
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hal-03619869 , version 1 (25-03-2022)


  • HAL Id : hal-03619869 , version 1


Nadia Yacoubi Ayadi, Catherine Faron, Franck Michel, Fabien Gandon, Olivier Corby. A Model for Meteorological Knowledge Graphs: Application to Météo-France Data. ICWE 2022- 22nd International Conference on Web Engineering, Jul 2022, Bari, Italy. ⟨hal-03619869⟩
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