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Spatial Linked Data: Infrastructures - Coded with LATEX

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Spatial Linked Data: Infrastructures - Coded with LATEX
Alternativer Titel
Geo-Spatial meets Linked Data: open source solutions for semantic spatial data exchange
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Abstract
The Ontology discipline made its way into the Computer Science domain in the 1990s, filling a gap in the architecture aspect of a still infant engineering domain. Its most visible impact happened around the industry consortium Object Management Group (OMG), leading first to the Unified Modelling Language (UML) and later to the Model Driven Architecture (MDA). MDA became the base infrastructure of data architectures and exchange mechanisms specified by institutions such as the Open Geo-spatial Consortium (OGC) or the European Commission (through the INPIRE directive). However, a parallel path has been treaded by the World Wide Web Consortium (W3C). First with the specification of the Resource Description Framework (RDF), a new paradigm for data encoding leveraged on the WWW, and later with the Web Ontology Language (OWL), a pragmatic approach to ontology encoding, building on RDF. This infrastructure developed by the W3C became known as the Semantic Web, and also as Linked Data, for the innovative paradigm through which it connects disparate data sources and data domains. The OGC would eventually approach the semantic web, specifying GeoSPARQL in 2013, an ontology and query language for linked geo-spatial data. However, technologies supporting this new standard were slow in materialising. More recently, the specification by the OGC of a new set of data standards based on the OpenAPI technology set out a clear path for the convergence of geo-spatial data with the Semantic Web. New software is emerging, opening an entirely new world to geo-spatial data provision, a clear step forwards in practically, usability and semantics. This address starts by reviewing the core concepts of the Semantic Web and then reviews state-of-the-art software for the management, publication and exploration of linked geo-spatial data. This addressed is targeted at SDI professionals and data scientists wishing to upgrade the semantics of the data they create and use.