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GeoTriples: Transforming Geospatial Data into RDF Graphs Using R2RML and RML Mappings

25 Pages Posted: 12 Sep 2018 First Look: Accepted

See all articles by Kostis Kyzirakos

Kostis Kyzirakos

National and Kapodistrian University of Athens - Department of Informatics and Telecommunications

Dimitrianos Savva

National and Kapodistrian University of Athens - Department of Informatics and Telecommunications

Ioannis Vlachopoulos

National and Kapodistrian University of Athens - Department of Informatics and Telecommunications

Alexandros Vasileiou

National and Kapodistrian University of Athens - Department of Informatics and Telecommunications

Nikolaos Karalis

National and Kapodistrian University of Athens - Department of Informatics and Telecommunications

Manolis Koubarakis

National and Kapodistrian University of Athens - Department of Informatics and Telecommunications

Stefan Manegold

Netherlands Organisation for Scientific Research - Database Architectures Group

Abstract

A lot of geospatial data has become available at no charge in many countries recently. Geospatial data that is currently made available by government agencies usually do not follow the linked data paradigm. In the few cases where government agencies do follow the linked data paradigm (e.g., Ordnance Survey in the United Kingdom), specialized scripts have been used for transforming geospatial data into RDF. In this paper we present the open source tool GeoTriples which generates and processes extended R2RML and RML mappings that transform geospatial data from many input formats into RDF. GeoTriples allows the transformation of geospatial data stored in raw files (shapefiles, CSV, KML, XML, GML and GeoJSON) and spatially-enabled RDBMS (PostGIS and MonetDB) into RDF graphs using well-known vocabularies like GeoSPARQL and stSPARQL, but without being tightly coupled to a specific vocabulary. GeoTriples has been developed in European projects LEO and Melodies and has been used to transform many geospatial data sources into linked data. We study the performance of GeoTriples experimentally using large publicly available geospatial datasets, and show that GeoTriples is very efficient and scalable especially when its mapping processor is implemented using Apache Hadoop.

Keywords: geospatial data, RDF, SPARQL, R2RML mappings, RML mappings, Apache Hadoop

Suggested Citation

Kyzirakos, Kostis and Savva, Dimitrianos and Vlachopoulos, Ioannis and Vasileiou, Alexandros and Karalis, Nikolaos and Koubarakis, Manolis and Manegold, Stefan, GeoTriples: Transforming Geospatial Data into RDF Graphs Using R2RML and RML Mappings (September 12, 2018). Journal of Web Semantics First Look . Available at SSRN: https://ssrn.com/abstract=3248492 or http://dx.doi.org/10.2139/ssrn.3248492

Kostis Kyzirakos (Contact Author)

National and Kapodistrian University of Athens - Department of Informatics and Telecommunications ( email )

Panepistimiopolis
Ilisia
Athens, GR15784
Greece

Dimitrianos Savva

National and Kapodistrian University of Athens - Department of Informatics and Telecommunications

Panepistimiopolis
Ilisia
Athens, GR15784
Greece

Ioannis Vlachopoulos

National and Kapodistrian University of Athens - Department of Informatics and Telecommunications

Panepistimiopolis
Ilisia
Athens, GR15784
Greece

Alexandros Vasileiou

National and Kapodistrian University of Athens - Department of Informatics and Telecommunications

Panepistimiopolis
Ilisia
Athens, GR15784
Greece

Nikolaos Karalis

National and Kapodistrian University of Athens - Department of Informatics and Telecommunications

Panepistimiopolis
Ilisia
Athens, GR15784
Greece

Manolis Koubarakis

National and Kapodistrian University of Athens - Department of Informatics and Telecommunications ( email )

Panepistimiopolis
Ilisia
Athens, GR15784
Greece

Stefan Manegold

Netherlands Organisation for Scientific Research - Database Architectures Group

Science Park 123
Amsterdam
Netherlands

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