Querying Brussels Spatiotemporal Linked Open Data

Chentout, Kevin; Vaisman, Alejandro

doi:10.1007/978-3-642-41033-8_48

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“…Note: A preliminary version of this paper has been published as two extended abstracts [14,15], briefly describing the project's features. The present paper substantially expands and updates such work, providing a full, detailed explanation of each step of the methodology and a comprehensive set of queries that shows the functionality and usefulness of the case study.…”

Section: Introductionmentioning

confidence: 99%

Mapping Spatiotemporal Data to RDF: A SPARQL Endpoint for Brussels

Vaisman

Chentout²

2019

IJGI

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This paper describes how a platform for publishing and querying linked open data for the Brussels Capital region in Belgium is built. Data are provided as relational tables or XML documents and are mapped into the RDF data model using R2RML, a standard language that allows defining customized mappings from relational databases to RDF datasets. In this work, data are spatiotemporal in nature; therefore, R2RML must be adapted to allow producing spatiotemporal Linked Open Data.Data generated in this way are used to populate a SPARQL endpoint, where queries are submitted and the result can be displayed on a map. This endpoint is implemented using Strabon, a spatiotemporal RDF triple store built by extending the RDF store Sesame. The first part of the paper describes how R2RML is adapted to allow producing spatial RDF data and to support XML data sources. These techniques are then used to map data about cultural events and public transport in Brussels into RDF. Spatial data are stored in the form of stRDF triples, the format required by Strabon. In addition, the endpoint is enriched with external data obtained from the Linked Open Data Cloud, from sites like DBpedia, Geonames, and LinkedGeoData, to provide context for analysis. The second part of the paper shows, through a comprehensive set of the spatial extension to SPARQL (stSPARQL) queries, how the endpoint can be exploited.

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Section: Introductionmentioning

confidence: 99%