GeoSPARQL

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GeoSPARQL is a standard for representation and querying of geospatial linked data for the Semantic Web from the Open Geospatial Consortium (OGC).[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:

Example

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 / 38.913574; -77.089005 and [ ⚑ ] 38°53′11″N 77°01′48″W / 38.886321°N 77.029953°W / 38.886321; -77.029953?"[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 use in GeoSPARQL

RCC8 has been implemented in GeoSPARQL as described below:

A graphical representation of Region Connection Calculus (RCC: Randell, Cui and Cohn, 1992) and the links to the equivalent naming by the Open Geospatial Consortium (OGC) with their equivalent URIs.
A graphical representation of Region Connection Calculus (RCC: Randell, Cui and Cohn, 1992) and the links to the equivalent naming by the Open Geospatial Consortium (OGC) with their equivalent URIs.

Implementations

There are (almost) no complete implementations of GeoSPARQL, there are, however partial or vendor implementations of GeoSPARQL. Currently there are the following implementations:

Apache Marmotta
GeoSPARQL was implemented in the context of the Google Summer of Code 2015.[7] on Apache Marmotta; it uses PostGIS, and it is available just for PostgreSQL.
Parliament
Parliament has an almost complete implementation of GeoSPARQL by using JENA and a modified ARQ query processor.[8]
Eclipse RDF4J
Eclipse RDF4J is an open-source Java framework for scalable RDF processing, storage, reasoning and SPARQL querying. It offers support for a large subset of GeoSPARQL functionality.[9]
Strabon
Strabon is an open-source semantic spatiotemporal RDF store that supports two popular extensions of SPARQL: stSPARQL and GeoSPARQL. Strabon is built by extending the well-known RDF store Sesame and extends Sesame's components to manage thematic, spatial and temporal data that is stored in the backend RDBMS. It has been fully tested with PostgreSQL (with PostGIS and PostgreSQL-Temporal extensions[10]) and MonetDB (with geom[11] module).
OpenSahara uSeekM IndexingSail Sesame Sail plugin
uSeekM IndexingSail uses a PostGIS installation to deliver GeoSPARQL. They deliver partial implementation of GeoSPARQL along with some vendor prefixes.[12][13]
Oracle Spatial and Graph
GraphDB
GraphDB is an enterprise ready Semantic Graph Database, compliant with W3C Standards. Semantic graph databases (also called RDF triplestores) provide the core infrastructure for solutions where modelling agility, data integration, relationship exploration and cross-enterprise data publishing and consumption are important.
Stardog
Stardog is an enterprise data unification platform built on smart graph technology: query, search, inference, and data virtualization.

Submission

The GeoSPARQL standard was submitted to the OGC by:

Related work

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.

See also

References

  1. ↑ Battle & Kolas 2012, p. 355.
  2. ↑ Battle & Kolas 2012, p. 358.
  3. ↑ Goodwin, John (26 April 2013). "GeoSPARQL and Ordnance Survey Linked Data". https://johngoodwin225.wordpress.com/2013/04/26/geosparql-and-ordnance-survey-linked-data/. 
  4. ↑ Gemma (3 June 2013). "New Linked Data service launches". http://blog.ordnancesurvey.co.uk/2013/06/new-linked-data-service-launches/. 
  5. ↑ "Imprint". AKSW. 2012-05-18. http://linkedgeodata.org/Imprint. 
  6. ↑ Battle & Kolas 2012, p. 363.
  7. ↑ https://wiki.apache.org/marmotta/GSoC/2015/MARMOTTA-584
  8. ↑ http://parliament.semwebcentral.org/
  9. ↑ http://docs.rdf4j.org/programming/#_geosparql
  10. ↑ https://github.com/jeff-davis/PostgreSQL-Temporal
  11. ↑ https://www.monetdb.org/Documentation/Extensions/GIS
  12. ↑ https://dev.opensahara.com/projects/useekm/wiki/IndexingSail#GeoSPARQL
  13. ↑ https://dev.opensahara.com/projects/useekm/wiki/GeoReference

External links




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