Paper Info
Title
On modeling linked open statistical data.
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.
Year
DOI
Venue
2019
10.1016/j.websem.2018.11.002
Journal of Web Semantics
Keywords
Field
DocType
Open data,Linked open statistical data,Modeling challenges,Delphi method
Publication,Theoretical definition,Open data,Data modeling,Best practice,Information retrieval,Computer science,Linked data,Delphi method,Data cube
Journal
Volume
ISSN
Citations 
55
1570-8268
0
PageRank 
References 
Authors
0.34
17
3
Name
Order
Citations
PageRank
Evangelos Kalampokis119919.11
Dimitris Zeginis219411.72
Tarabanis Konstantinos395893.27