Semantic Web Standards for Publishing and Integrating Open Data

Semantic Web Standards for Publishing and Integrating Open Data

Axel Polleres (Vienna University of Economics and Business (WU Wien), Austria) and Simon Steyskal (Vienna University of Economics and Business (WU Wien), Austria & Vienna University of Technology (TU Wien), Austria)
DOI: 10.4018/978-1-4666-6236-0.ch003
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Abstract

The World Wide Web Consortium (W3C) as the main standardization body for Web standards has set a particular focus on publishing and integrating Open Data. In this chapter, the authors explain various standards from the W3C's Semantic Web activity and the—potential—role they play in the context of Open Data: RDF, as a standard data format for publishing and consuming structured information on the Web; the Linked Data principles for interlinking RDF data published across the Web and leveraging a Web of Data; RDFS and OWL to describe vocabularies used in RDF and for describing mappings between such vocabularies. The authors conclude with a review of current deployments of these standards on the Web, particularly within public Open Data initiatives, and discuss potential risks and challenges.
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Background: Semantic Web Standards And Open Data

RDF

The Resource Description Framework (RDF) is the basic data model for the Semantic Web. It is built upon one of the simplest structures for representing data: a directed labelled graph. An RDF graph is described by a set of triples of the form <Subject Predicate Object>, also called statements, which may be viewed as connecting subjects to objects via edges labelled by the predicates. Since any of these can be - dereferenceable - URIs, these edges may be equally viewed as “typed” links between resources and other resources, where resources - unlike in the HTML Web, are no longer restricted to be documents, but arbitrary entities, that can be identified by URIs.

RDF's flat graph-like representation has the advantage of abstracting away from the data schema, and thus promises to allow for easier integration than customized XML data in different XML dialects: whereas the integration of different XML languages requires the transformation between different tree structures using transformation languages such as XSLT (Kay, 2007) or XQuery (Chamberlin et al., 2007), different RDF graphs can simply be stored and queried alongside, and as soon as they share common URIs, form a joint graph upon a simple merge operation accumulating their respective triples in one joint graph. While the normative syntax to exchange RDF, RDF/XML (Beckett and McBride, 2004), is an XML dialect itself, there are various other serialization formats for RDF, such as RDFa (Adida et al., 2008), a format that allows one to embed RDF within (X)HTML, or non-XML representations such as the more readable Turtle (Beckett and Berners-Lee, 2008) syntax; likewise RDF stores, that is, special databases for RDF, normally use their own, proprietary internal representations of triples, that do not relate to XML. Various RDF stores that can store and handle RDF data efficiently and at large scale are nowadays available off-the-shelf, both commercial systems and academic ones, such as YARS2 (Harth et al., 2007), Jena TDB (http://www.openrdf.org/) to name a few. For an overview of RDF Stores, see also (Haslhofer et al., 2011); a recent article also discusses the use of NoSQL graph databases to store and process RDF (Cudré-Mauroux et al., 2013).

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