“Geospatial technology, information, and services are addressing some of the major priorities of our nations, adding value to productivity, reducing costs and enabling GDP growth in the process.”

Prof. Arup Dasgupta, in Geospatial World, May 2013

In recent years, Semantic Web technologies have strengthened their position in the areas of data and knowledge management. Standards for organizing and querying semantic information, such as RDF(S) and SPARQL are adopted by large academic communities, while corporate vendors adopt semantic technologies to organize, expose, exchange and retrieve their datasets as Linked Data.

Moreover, a large number of currently available datasets (both RDF and conventional) contain geospatial information,  which is of high importance in several application scenarios, e.g., navigation, tourism, or social media. Examples include DBpedia, Geonames, OSM and its RDF counterpart, LinkedGeoData. RDF stores have become robust and scalable enough to support volumes of billions of records (RDF triples) but traditional geospatial data management systems still significantly outperform them in efficiency and scalability. On the other hand, GIS systems can benefit from Linked Data principles (e.g. schema agility, interoperability).

Recently, GeoSPARQL has emerged as a promising standard from OGC (Open Geospatial Consortium) for geospatial RDF that targets the standardized geospatial RDF data modeling and querying. A great number of tools and libraries have been developed that allow for handling (storing, querying, visualizing, etc.) Linked Data, however only a few approaches started to focus on geospatial RDF data management. Integrating Semantic Web with geospatial data management requires the scientific community to address the two following challenges. First, the definition of proper standards, vocabularies and methodologies for representing, transforming and mapping geospatial information according to RDF(S) and SPARQL protocols that also conform to the principles of established geospatial standards. Second, the development of technologies for efficient storage, robust indexing, processing, reasoning, querying and visualization of semantically organized geospatial data.

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