The Météo-France RDF Weather dataset (WeKG-MF) is an RDF knowledge graph that provides access to meteorological measurements provided by 62 Météo-France weather stations located in different regions in metropolitan France and overseas departments. The dataset incorporates measurements of several weather parameters such as wind direction and speed, air pressure, precipitations, humidity and air temperature. These measurements are provided every three hours per day.
The WeKG-MF namespace is http://ns.inria.fr/meteo/.
See the WeKG ontology repository.
A new version of WeKG-MF is now available ! Please download RDF dumps (in Turtle syntax) from Zenodo :
It can also be queried through our Virtuoso SPARQL endpoint: http://weakg.i3s.unice.fr/sparql
In order to re-use the WeKG dataset and create your local SPARQL endpoint, we recommend to use the openLink Virtuoso Docker Image. We provide scripts availabe in Lifting-dataset/virtuoso repository to upload the downloaded WeKG RDF datasetin different named graphs. Use the script import-weather-dataset as main entry point.
Several SPARQL queries are provided in sparql-examples directory and serves as illustrative examples showing how data is retreived from the WeKG graph. A Jupyter Notebook WeKG-MFQuerying.ipynb demonstrates how the results of some SPARQL queries can be used to generate visualizations very useful for experts in different domains.
Statistics about WeKG-MF is provided as follows :
| Named Graph | No. of RDF triples |
|---|---|
| http://ns.inria.fr/meteo/ontology | 193 |
| http://ns.inria.fr/meteo/vocab | 346 |
| http://ns.inria.fr/meteo/weatherstation | 794 |
| http://ns.inria.fr/meteo/observation/2021 | 20.604.775 |
| http://ns.inria.fr/meteo/observation/2020 | 20.868.650 |
| http://ns.inria.fr/meteo/observation/2019 | 20.832.677 |
| http://ns.inria.fr/meteo/observation/2018 | 19.684.672 |
| http://ns.inria.fr/meteo/observation/2017 | 20.539.699 |
We provide a fully automatic pipeline that enables us the maintenance and update of the WeKG graph with new weather data from the Météo-France organism. This pipeline allowes to generate the WeKG dataset and update it over time. The pipeline involves several steps including the preprocessing and loading data in MongoDB database as JSON collections and their transformation in RDF data. The Morph-xR2RML tool allows the generation of RDF data.
The script run_pipeline.sh available in Lifting-dataset directory is the main entry point of the pipeline.
The script run_pipeline.sh needs 3 arguments:
-
JSON collection name: the CSV files of weather report downloaded from the Meteo-France Website are loaded as JSON collection in MongoDB.
-
Mapping rules file : mappings files are available in
Lifting-dataset/xr2rml, e.g., mapping_observation_tpl.ttl -
Output file name : e.g,
rdf-dataset-yyyy.ttl
Example :
./run_pipeline.sh collection022021 mapping_observation_tpl.ttl rdf-dataset-02-2021.ttlMore informations are available in Lifting-dataset repository.