We present Ontop-spatial, a geospatial extension of the wellknown OBDA system Ontop, that leverages the technologies of geospatial databases and enables GeoSPARQL-to-SQL translation. We showcase the functionalities of the system in real-world use cases which require data integration of di erent geospatial sources. During the recent years, the amount of geospatial data in the Web of Data has increased. This is because geospatial data practitioners coming from various domains (e.g., earth scientists, geologists, civil engineers) that are involved in the processing of geospatial data, also publish them as RDF to increase its value by combining it with other data. As a result, the Semantic Web community became active proposing data models, query languages and applications for the representation, modeling and visualization of linked geospatial data [5]. These e orts have been strengthened by the establishment of the Open Geospatial Consortium (OGC) standard GeoSPARQL [1], a geospatial extension of RDF and SPARQL. At the same time, similar extensions of RDF and SPARQL were also proposed, such as the framework of stRDF and stSPARQL which extends RDF and SPARQL with space and time [6,3]. RDF stores with geospatial support were also implemented, such as Parliament, uSeekM and Virtuoso, that implement a subset of GeoSPARQL, and Strabon [6], that implements both GeoSPARQL and stSPARQL. Despite the long tradition of research in geospatial relational databases, and the existence of Ontology-Based Data Access (OBDA) systems that o er on-the- y SPARQL-to-SQL translation based on ontologies and mappings (e.g., Ontop [9], Ultrawrap [10], Morph [8]), there was no OBDA system with GeoSPARQL support. In [2], we describe how we extended Ontop [9] with geospatial support and implemented Ontop-spatial, a geospatial extension of the system Ontop. The development of Ontop-spatial was initially motivated by the Statoil use case in the context of the EU FP7 project OPTIQUE6, in order to address the
issue of creating virtual RDF graphs on top of large relational databases that
contain geometries and get frequently updated. Ontop-spatial is being used in
the Urban accountant, Land management, and Crisis Mapping services of the
EU FP7 project MELODIES7. More recently, Ontop-spatial has been used in
the maritime security domain by the German BMBF project EMSEC [
In this demo paper we focus on how Ontop-spatial can be used to integrate geospatial data from di erent sources and express rich queries that combine them. In Section 2 we present a technical overview of Ontop-spatial, explaining its compliance with other visualization tools that are useful for domain experts. In Section 3 we give an overview of our demonstration, presenting how Ontopspatial is used in land management and crisis mapping real-world scenarios. 2
Ontop-spatial8 extends Ontop to enable the on-the- y GeoSPARQL-to-SQL
translation on top of geospatial databases and thus becomes the rst OBDA
system with geospatial support. It is able to connect to a geospatial database
(currently PostGIS or Spatialite) and create virtual geospatial RDF graphs on
top of it, using ontologies and mappings. It supports the following components of
GeoSPARQL: Core, Topology Vocabulary, Geometry topology extension, RDFS
entailment and a subset of Geometry Extension. To the best of our knowledge,
it is also the rst GeoSPARQL implementation that supports the query rewrite
extension of GeoSPARQL. In [
For example, the GeoSPARQL query in Listing 1.1 retrieves buildings that are a ected by oods (i.e., they have intersecting geometries with the ood geometries). The query described in Listing 1.2 can also be evaluated in Ontopspatial returning the same results, as it gets transformed internally to the query in 1.1, according to the query rewrite extension of the GeoSPARQL speci cation.
SELECT DISTINCT ? name ? build ? type WHERE {? s1 f: type ? type . ? s1 geo : asWKT ? g1 . ? s2 geo : asWKT ? g2 . ? s2 rdf : type osm : Building . ? s2 osm : hasName ? name . ? s2 osm : buildingCategory ? build . FILTER ( geof : sfIntersects (? g1 , ? g2 )) } SELECT DISTINCT ? name ? build ? type WHERE { ? s1 f: type ? type . ? s2 rdf : type osm : Building . ? s2 osm : hasName ? name . ? s2 osm : buildingCategory ? build . ? s1 geo : sfIntersects ? s2 }
The demonstration of Ontop-spatial will be based on the real-world scenarios of land management and crisis mapping studied in the EU FP7 project MELODIES. These use cases are led by the German company VISTA and the companies EOXPLORE and Terranea respectively.
In the land management scenario, we are interested in discovering agricultural elds that intersect with protected areas. To achieve this, we need to combine information from the following geospatial datasets: { Agricultural elds. This dataset contains information about elds, i.e., their geographic location, their name, code, etc. This is in-house data, but we can use a sample of it for the demonstration. { Protected areas. This is a set of di erent datasets that describe categories of protected areas. Most of this data is in-house. { Corine Land Cover (CLC). This dataset is released as open data by the European Environment Agency (EEA) and it contains information about the land cover of various countries of Europe. In the context of this use case, we focus on categories that represent protected areas.
In the crisis mapping scenario, we are mainly interested in information about oods, which is stored originally in a PostGIS database9 maintained by EOXPLORE and Terranea. This database consists of a table that contains only basic information about oods such as the location, date and some information about the region and the country. We enrich this dataset by integrating the following open datasets: { The Global Administrative Areas dataset10 that contains information about all levels of administrative divisions worldwide. { The Corine Land Cover dataset, focusing in areas that are characterized as \Water bodies". { The Open Street Maps dataset (OSM). OSM also contains some categories for water bodies, such as rivers and lakes, as well as points of interest.
All datasets described in both use cases are originally in Shape le format, except for the oods data that are relational (stored in a PostGIS database).
In the demonstration we will show how we can integrate geospatial data coming from di erent sources and then pose rich queries combining them, in both of these use cases.
Integration. All Shape les are imported to a database. In case a database preexists (e.g., the oods database), so we import the additional shape les there. Every shape le will be imported to a correspoding table, and the columns of this table will be the same as the attributes of the shape le.
Ontology. We need to construct an ontology to model the information that we want to map to RDF. In order to exploit the geospatial features, this ontology should be an extension of the GeoSPARQL ontology11.
Mappings. Mappings describe how relational data can be translated to RDF.
A mapping le is constructed, using either the R2RML mapping languages or
the native mapping language of Ontop. The Protege plugin of Ontop provides a
user-friendly graphical interface for editing and managing mappings.
Posing rich geospatial queries. A user can create a repository using either
the embedded Sesame-based web interface of Ontop or the visual query interface
the Optique platform [
Visualization of results. Ontop-spatial inherits the ability of Ontop to be
used as a SPARQL endpoint. So the results can be visualized on the map using
Sextant [
The queries described in 1.1 and 1.2 are examples of the queries used in the crisis mapping scenario. A more detailed description of the demonstration is given in the online video: https://youtu.be/F5_2Zxi5_e8.
Acknowledgement. This work is partially supported by the EU FP7 projects Optique (318338) and MELODIES (603525).