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Title: On modeling linked open statistical data
Authors: Kalampokis, Evangelos
Zeginis, Dimitris
Tarabanis, Konstantinos
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Open data
Linked open statistical data
Modeling challenges
Delphi method
Issue Date: Mar-2019
Publisher: Elsevier
Source: Journal of Web Semantics
Volume: 55
First Page: 56
Last Page: 68
Abstract: A major part of Open Data concerns statistics such as economic and social indicators. Statistical data are structured in a multidimensional manner creating data cubes. Recently, National Statistical Institutes and public authorities adopted the Linked Data paradigm to publish their statistical data on the Web. Many vocabularies have been created to enable modeling data cubes as RDF graphs, and thus creating Linked Open Statistical Data (LOSD). However, the creation of LOSD remains a demanding task mainly because of modeling challenges related either to the conceptual definition of the cube, or to the way of modeling cubes as linked data. The aim of this paper is to identify and clarify (a) modeling challenges related to the creation of LOSD and (b) approaches to address them. Towards this end, nine LOSD experts were involved in an interactive feedback collection and consensus-building process that was based on Delphi method. We anticipate that the results of this paper will contribute towards the formulation of best practices for creating LOSD, and thus facilitate combining and analyzing statistical data from diverse sources on the Web.
ISSN: 1570-8268
Other Identifiers: 10.1016/j.websem.2018.11.002
Appears in Collections:Department of Business Administration

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