Adding value to Linked Open Data using a multidimensional model approach based on the RDF Data Cube vocabulary

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Title: Adding value to Linked Open Data using a multidimensional model approach based on the RDF Data Cube vocabulary
Authors: Escobar Esteban, María Pilar | Candela Romero, Gustavo | Trujillo Mondéjar, Juan Carlos | Marco Such, Manuel | Peral Cortés, Jesús
Research Group/s: Lucentia | Transducens
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: Linked Open Data | Multidimensional modelling | Conceptual modelling | RDF Data | Cube vocabulary | Semantic web | Big data
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: Feb-2020
Publisher: Elsevier
Citation: Computer Standards & Interfaces. 2020, 68: 103378. doi:10.1016/j.csi.2019.103378
Abstract: Most organisations using Open Data currently focus on data processing and analysis. However, although Open Data may be available online, these data are generally of poor quality, thus discouraging others from contributing to and reusing them. This paper describes an approach to publish statistical data from public repositories by using Semantic Web standards published by the W3C, such as RDF and SPARQL, in order to facilitate the analysis of multidimensional models. We have defined a framework based on the entire lifecycle of data publication including a novel step of Linked Open Data assessment and the use of external repositories as knowledge base for data enrichment. As a result, users are able to interact with the data generated according to the RDF Data Cube vocabulary, which makes it possible for general users to avoid the complexity of SPARQL when analysing data. The use case was applied to the Barcelona Open Data platform and revealed the benefits of the application of our approach, such as helping in the decision-making process.
Sponsor: This work was supported in part by the Spanish Ministry of Science, Innovation and Universities through the Project ECLIPSE-UA under grant RTI2018-094283-B-C32.
URI: http://hdl.handle.net/10045/97097
ISSN: 0920-5489 (Print) | 1872-7018 (Online)
DOI: 10.1016/j.csi.2019.103378
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2019 Elsevier B.V.
Peer Review: si
Publisher version: https://doi.org/10.1016/j.csi.2019.103378
Appears in Collections:INV - TRANSDUCENS - Artículos de Revistas
INV - LUCENTIA - Artículos de Revistas

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