Linked Open Statistical Metadata

Linked Open Statistical Metadata

Franck Cotton, Daniel Gillman
Copyright: © 2017 |Pages: 24
DOI: 10.4018/978-1-5225-2512-7.ch012
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Abstract

Linked Open Statistical Metadata (LOSM) is Linked Open Data (LOD) applied to statistical metadata. LOD is a model for identifying, structuring, interlinking, and querying data published directly on the web. It builds on the standards of the semantic web defined by the W3C. LOD uses the Resource Description Framework (RDF), a simple data model expressing content as predicates linking resources between them or with literal properties. The simplicity of the model makes it able to represent any data, including metadata. We define statistical data as data produced through some statistical process or intended for statistical analyses, and statistical metadata as metadata describing statistical data. LOSM promotes discovery and the meaning and structure of statistical data in an automated way. Consequently, it helps with understanding and interpreting data and preventing inadequate or flawed visualizations for statistical data. This enhances statistical literacy and efforts at visualizing statistics.
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Semantic Web And Resource Description Framework

This section gives a brief primer on RDF, a W3C standard that facilitates the exchange of structured data on the Internet. Based on a simple subject-predicate-object model commonly referred to as “triples”, it allows for a generic, standardized structuring of resources that can be used to model and disseminate everything from taxonomies to statistical observations to metadata records. The model used by RDF is also commonly referred to as a “graph model” consisting of “nodes” (which are vertices) and “edges” or “arcs”. See the Figure 1 below for an example.

The Web Ontology Language (OWL) (W3, 2013) is part of the Semantic Web technology stack in which RDF also resides. It is a logic-based language intended to represent formal knowledge of things on the Web. Though OWL is not the subject of this section, it is in wide use and can be used in situations for which RDF is not expressive enough to account for all the knowledge a system demands.

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