This repository contains the code used to generate the Semantic Trails Datasets (STDs). You can download the actual datasets from figshare, while you can query their RDF version at http://std.eurecom.fr/sparql.
We first downloaded from Wikidata the URIs of all the cities and the associated countries available on GeoNames, as well as their population, if available. To this purpose, we exploited the download_cities.py script.
The input file cities500.txt can be obtained here, while the output files cities.csv and population.csv are already available in the repository. If necessary, they can be updated by running python download_cities.py.
The format of cities.csv is the one required by the library reverse_geocoder. This file will be exploited by the create_std.py script.
We mapped the categories of the Foursquare taxonomy with the Schema.org vocabulary.
The script download_categories.py was used to download the identifiers of such categories from Foursquare, as well as their translations in English, French, and Italian. The obtained categories are listed in the file categories.csv. If necessary, it can be updated by running python download_categories.py.
The result of the mapping process is available in the mapping.csv file. The first column represents the Foursquare category identifier, the second one its English translation, while the third one the corresponding Schema.org URI. This file also contains some category names that were previously used by Foursquare, which we merged with the current identifier. The mapping.csv file will be exploited by the create_std.py script.
We constructed the STDs by means of the create_std.py script. It requires two input files, checkins.csv and venues.csv, in the same format of the Global-Scale Check-in Dataset, and the previously mentioned cities.csv and mapping.csv files. The resulting file can be created by simply running python create_std.py.
The resulting datasets are available on figshare, together with a description of the output format.
We converted the CSV datasets into RDF by using the convert_rdf.py script. The script takes one parameter, that is the name of the dataset without the extension. For example, python convert_rdf.py std_2013 will parse std_2013.csv and export it to std_2013.ttl. If a categories.csv file exists, it will also automatically attempt to open and parse it, process the categories, and then export it as categories.ttl.
In the following, we provide an example of our RDF model.
<http://std.eurecom.fr/checkin/75d2860eb8ad49789ef63e69e898d03237c67df4> a schema:CheckInAction ;
schema:agent <http://std.eurecom.fr/user/1> ;
schema:location <http://www.foursquare.com/v/597190d44b78c57f67ddd616> ;
schema:result <http://std.eurecom.fr/trail/1> ;
schema:startTime "2017-10-03T14:44:00+03:00"^^xsd:dateTime .
<http://std.eurecom.fr/user/1> a schema:Person .
<http://www.foursquare.com/v/597190d44b78c57f67ddd616> a schema:CivicStructure,
schema:Place ;
dbo:category <http://std.eurecom.fr/category/4bf58dd8d48988d198941735> ;
schema:address [ a schema:PostalAddress ;
schema:addressCountry wd:Q43 ;
schema:addressLocality wd:Q894012 ] .
<http://std.eurecom.fr/category/4bf58dd8d48988d198941735> a skos:Concept ;
rdfs:label "College Academic Building"@en,
"Bâtiment universitaire"@fr,
"Edificio universitario accademico"@it ;
rdfs:isDefinedBy <https://developer.foursquare.com/docs/resources/categories> .
- Diego Monti diego.monti@polito.it
- Enrico Palumbo enrico.palumbo@ismb.it
- Giuseppe Rizzo giuseppe.rizzo@ismb.it
- Raphaël Troncy raphael.troncy@eurecom.fr
- Thibault Ehrhart thibault.ehrhart@eurecom.fr
- Maurizio Morisio maurizio.morisio@polito.it