GeoTriples: Transforming geospatial data into RDF graphs using R2RML and RML mappings

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.

Introduction

In the last few years, the area of linked geospatial data has received attention as researchers and practitioners have started tapping the wealth of existing geospatial information and making it available on the Web [[1], [2]]. As a result, the linked open data (LOD) cloud has been slowly populated with geospatial data. For example, Great Britain’s national mapping agency, Ordnance Survey, has been the first national mapping agency that has made various kinds of geospatial data from Great Britain available as linked open data.¹ Similarly, projects TELEIOS,² LEO,³ MELODIES⁴ and Copernicus App Lab,⁵ in which our research groups participated, published a number of geospatial datasets that are Earth observation products e.g., CORINE Land Cover and Urban Atlas.⁶ Also, the Spatial Data on the Web working group⁷ created jointly by the Open Geospatial Consortium (OGC) and the World Wide Web Consortium (W3C) has produced in 2017 five relevant working notes on best practices, use cases and requirements, Earth observation data, spatio-temporal data cubes and coverages as linked data.

Geospatial data can come in vector or raster form and are usually accompanied by metadata. Vector data, available in formats such as ESRI shapefiles, KML, and GeoJSON documents, can be accessed either directly or via Web Services such as the OGC Web Feature Service or the query language of a geospatial DBMS. Raster data, available in formats such as GeoTIFF, Network Common Data Form (netCDF) and Hierarchical Data Format (HDF), can be accessed either directly or via Web Services such as the OGC Web Coverage Processing Service (WCS) or the query language of an array DBMS, e.g., rasdaman⁸ or MonetDB/SciQL. Metadata about geospatial data are encoded in various formats ranging from custom XML schemas to domain specific standards like the OGC GML Application schema for EO products and the OGC Metadata Profile of Observations and Measurements. Automating the process of transforming input geospatial data to linked data has only been addressed by few works so far [[3], [4], [5], [6], [7]]. In many cases, for example in the wildfire monitoring and management application that we developed in TELEIOS [5], custom Python scripts were used for transforming all the necessary geospatial data into linked data.

In this paper we extend the mapping languages R2RML⁹ and RML¹⁰ with some new constructs that help to specify ways of transforming geospatial data from its original format into RDF. We also present the tool GeoTriples that generates automatically and processes extended R2RML and RML mappings for transforming geospatial data from various formats into RDF graphs. The input formats supported are spatially-enabled relational databases (PostGIS and MonetDB), ESRI shapefiles, XML documents following a given schema (hence GML documents as well), KML documents, JSON and GeoJSON documents and CSV documents. GeoTriples is a semi-automated tool that enables the automatic transformation of geospatial data into RDF graphs using state of the art vocabularies like GeoSPARQL [8], but at the same time it is not tightly coupled to a specific vocabulary. The transformation process comprises three steps. First, GeoTriples generates automatically extended R2RML or RML mappings for transforming data that reside in spatially-enabled databases or raw files into RDF. As an optional second step, the user may revise these mappings according to her needs e.g., to utilize a different vocabulary. Finally, GeoTriples processes these mappings and produces an RDF graph.

Users can store and query an RDF graph generated by GeoTriples using a geospatial RDF store like Strabon.¹¹ They can also interlink this graph with other linked geospatial data using tools like the temporal and geospatial extension of Silk¹² developed in our group [9] or the more recent tool Radon developed with the participation of our group [10]. For example, it might be useful to infer links involving topological relationships e.g., A geo:sfContains F where A is the area covered by a remotely sensed multispectral image I, F is a geographical feature of interest (field, lake, city etc.) and geo:sfContains is a topological relationship from the topology vocabulary extension of GeoSPARQL. The existence of this link might indicate that I is an appropriate image for studying certain properties of F.

It is often the case in applications that relevant geospatial data is stored in spatially-enabled relational databases (e.g., PostGIS) or files (e.g., shapefiles), and its owners do not want to explicitly transform it into linked data [[11], [12]]. For example, this might be because these data sources get frequently updated and/or are very large. If this is the case, GeoTriples is still very useful. GeoTriple users can use the generated mappings in the system Ontop-spatial to view their data sources virtually as linked data. Ontop-spatial is a geospatial extension of the Ontology-Based Data Access (OBDA) system Ontop¹³ developed by our group [13]. Ontop performs on-the-fly SPARQL-to-SQL translation on top of relational databases using ontologies and mappings. Ontop-spatial extends Ontop by enabling on-the-fly GeoSPARQL-to-SQL translation on top of geospatial databases. The experimental evaluation of [13] has shown that this approach is not only simpler for the users as it does not require transformation of data, but also more efficient in terms of query response time.

GeoTriples is an open source tool that has been developed in the context of the EU FP7 projects LEO and MELODIES mentioned in the beginning of this section. It is currently utilized in the EU Horizon 2020 project Copernicus App Lab where data from three Copernicus Services¹⁴ (Land, Marine and Atmosphere) are made available as linked data to aid their take-up by mobile developers.

The organization of the paper is as follows. Section 2 presents background information and discusses related work. In Section 3 we present the extensions to the mapping languages R2RML and RML for the geospatial domain. In Section 4 we present the architecture of GeoTriples and discuss how GeoTriples generates automatically mappings, and how these mappings are subsequently processed for transforming a geospatial data source into an RDF graph. Section 5 gives an example of translating an input shapefile into RDF, using the GeoTriples utilities. Section 6 presents an implementation of the mapping process of GeoTriples that uses Apache Hadoop. In Section 7 we perform a performance evaluation of the implementations of GeoTriples using publicly available geospatial data. We also compare GeoTriples with the similar tool TripleGeo. Finally, in Section 8, we conclude the paper and discuss future work.