GeoSPARQL is a model for representing and querying geospatial linked data for the Semantic Web. It is standardized by the Open Geospatial Consortium as OGC GeoSPARQL. [1] The definition of a small ontology based on well-understood OGC standards is intended to provide a standardized exchange basis for geospatial RDF data which can support both qualitative and quantitative spatial reasoning and querying with the SPARQL database query language. [2]
The Ordnance Survey Linked Data Platform uses OWL mappings for GeoSPARQL equivalent properties in its vocabulary. [3] [4] The LinkedGeoData data set is a work of the Agile Knowledge Engineering and Semantic Web (AKSW) research group at the University of Leipzig, [5] a group mostly known for DBpedia, that uses the GeoSPARQL vocabulary to represent OpenStreetMap data.
In particular, GeoSPARQL provides for:
The following example SPARQL query could help model the question "What is within the bounding box defined by 38°54′49″N 77°05′20″W / 38.913574°N 77.089005°W and 38°53′11″N 77°01′48″W / 38.886321°N 77.029953°W?" [6]
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>
SELECT ?what
WHERE {
?what geo:hasGeometry ?geometry .
FILTER(geof:sfWithin(?geometry,
"POLYGON((-77.089005 38.913574,-77.029953 38.913574,-77.029953 38.886321,-77.089005 38.886321,-77.089005 38.913574))"^^geo:wktLiteral))
}
RCC8 has been implemented in GeoSPARQL as described below:
There are (almost) no complete implementations of GeoSPARQL; however, there are partial or vendor implementations of GeoSPARQL. Currently there are the following implementations:
Benchmarking GeoSPARQL 1.0 and geospatial-enabled triplestores, in general, has been conducted using several approaches. One can distinguish between performance and compliance benchmarks. The former can reveal whether a triplestore gives a timely answer to a GeoSPARQL query and may or may not check the answer for correctness. The latter checks whether a triplestore gives compliant answers with respect to the definitions of the GeoSPARQL 1.0 standard irrespective of the time the query takes for execution.
Well-known geospatial performance benchmarks include the Geographica [17] and Geographica 2 [18] benchmarks which track the performance of predefined sets of queries on synthetic and real-world datasets. They each test a subset of GeoSPARQL query functions for performance. Another performance benchmark by Huang et al. [19] assessed the performance of GeoSPARQL-enabled triple stores as part of a spatial data infrastructure.
Compliance benchmarking of OGC standards is usually conducted as part of the OGC Team Engine Test Suite [20] which allows companies to obtain certification for implementing certain OGC specifications correctly. As of 2021, however, the OGC Team Engine does not provide a set of compliance tests to test GeoSPARQL compliance. Nevertheless, in 2021, Jovanovik et al. [21] developed the first comprehensive, reproducible GeoSPARQL Compliance benchmark in which nine different triple stores were initially tested. The results of these first compliance tests along with the software [22] are available on Github. [23]
The GeoSPARQL standard was submitted to the OGC by:
With regards to future work, the GeoSPARQL standard states:
Obvious extensions are to define new conformance classes for other standard serializations of geometry data (e.g. KML, GeoJSON). In addition, significant work remains in developing vocabularies for spatial data, and expanding the GeoSPARQL vocabularies with OWL axioms to aid in logical spatial reasoning would be a valuable contribution. There are also large amounts of existing feature data represented in either a GML file (or similar serialization) or in a datastore supporting the general feature model. It would be beneficial to develop standard processes for converting (or virtually converting and exposing) this data to RDF.
In 2019, the OGC's GeoSemantics Domain Working Group [24] set out to assess the current usage of GeoSPARQL in different domains in the White Paper "OGC Benefits of Representing Spatial Data Using Semantic and Graph Technologies" [25] and collected initial feature requests to extend GeoSPARQL.
This led to the re-establishment of the GeoSPARQL Standards Working Group with a newly formed working group charter in September 2020. [26] [27] The group is working towards a new release of the GeoSPARQL standard, with non-breaking changes - GeoSPARQL 1.1 - in the summer of 2021, the development of which can be followed on Github.
At the GeoLD workshop 2021, held as part of the Extended Semantic Web Conference 2021, an outline of the additions which are likely to be present in GeoSPARQL 1.1 has been presented. [28] The changes have been further consolidated and summarized in a publication in the ISPRS International Journal of GeoInformation. [29]
GeoSPARQL is a model for representing and querying geospatial linked data for the Semantic Web. It is standardized by the Open Geospatial Consortium as OGC GeoSPARQL. [1] The definition of a small ontology based on well-understood OGC standards is intended to provide a standardized exchange basis for geospatial RDF data which can support both qualitative and quantitative spatial reasoning and querying with the SPARQL database query language. [2]
The Ordnance Survey Linked Data Platform uses OWL mappings for GeoSPARQL equivalent properties in its vocabulary. [3] [4] The LinkedGeoData data set is a work of the Agile Knowledge Engineering and Semantic Web (AKSW) research group at the University of Leipzig, [5] a group mostly known for DBpedia, that uses the GeoSPARQL vocabulary to represent OpenStreetMap data.
In particular, GeoSPARQL provides for:
The following example SPARQL query could help model the question "What is within the bounding box defined by 38°54′49″N 77°05′20″W / 38.913574°N 77.089005°W and 38°53′11″N 77°01′48″W / 38.886321°N 77.029953°W?" [6]
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>
SELECT ?what
WHERE {
?what geo:hasGeometry ?geometry .
FILTER(geof:sfWithin(?geometry,
"POLYGON((-77.089005 38.913574,-77.029953 38.913574,-77.029953 38.886321,-77.089005 38.886321,-77.089005 38.913574))"^^geo:wktLiteral))
}
RCC8 has been implemented in GeoSPARQL as described below:
There are (almost) no complete implementations of GeoSPARQL; however, there are partial or vendor implementations of GeoSPARQL. Currently there are the following implementations:
Benchmarking GeoSPARQL 1.0 and geospatial-enabled triplestores, in general, has been conducted using several approaches. One can distinguish between performance and compliance benchmarks. The former can reveal whether a triplestore gives a timely answer to a GeoSPARQL query and may or may not check the answer for correctness. The latter checks whether a triplestore gives compliant answers with respect to the definitions of the GeoSPARQL 1.0 standard irrespective of the time the query takes for execution.
Well-known geospatial performance benchmarks include the Geographica [17] and Geographica 2 [18] benchmarks which track the performance of predefined sets of queries on synthetic and real-world datasets. They each test a subset of GeoSPARQL query functions for performance. Another performance benchmark by Huang et al. [19] assessed the performance of GeoSPARQL-enabled triple stores as part of a spatial data infrastructure.
Compliance benchmarking of OGC standards is usually conducted as part of the OGC Team Engine Test Suite [20] which allows companies to obtain certification for implementing certain OGC specifications correctly. As of 2021, however, the OGC Team Engine does not provide a set of compliance tests to test GeoSPARQL compliance. Nevertheless, in 2021, Jovanovik et al. [21] developed the first comprehensive, reproducible GeoSPARQL Compliance benchmark in which nine different triple stores were initially tested. The results of these first compliance tests along with the software [22] are available on Github. [23]
The GeoSPARQL standard was submitted to the OGC by:
With regards to future work, the GeoSPARQL standard states:
Obvious extensions are to define new conformance classes for other standard serializations of geometry data (e.g. KML, GeoJSON). In addition, significant work remains in developing vocabularies for spatial data, and expanding the GeoSPARQL vocabularies with OWL axioms to aid in logical spatial reasoning would be a valuable contribution. There are also large amounts of existing feature data represented in either a GML file (or similar serialization) or in a datastore supporting the general feature model. It would be beneficial to develop standard processes for converting (or virtually converting and exposing) this data to RDF.
In 2019, the OGC's GeoSemantics Domain Working Group [24] set out to assess the current usage of GeoSPARQL in different domains in the White Paper "OGC Benefits of Representing Spatial Data Using Semantic and Graph Technologies" [25] and collected initial feature requests to extend GeoSPARQL.
This led to the re-establishment of the GeoSPARQL Standards Working Group with a newly formed working group charter in September 2020. [26] [27] The group is working towards a new release of the GeoSPARQL standard, with non-breaking changes - GeoSPARQL 1.1 - in the summer of 2021, the development of which can be followed on Github.
At the GeoLD workshop 2021, held as part of the Extended Semantic Web Conference 2021, an outline of the additions which are likely to be present in GeoSPARQL 1.1 has been presented. [28] The changes have been further consolidated and summarized in a publication in the ISPRS International Journal of GeoInformation. [29]