Please use this identifier to cite or link to this item: http://hdl.handle.net/11144/4315
Title: A Fuzzy Approach for Data Quality Assessment of Linked Datasets
Authors: Arruda, Narciso
Alcântara, João
Vidal, Vânia
Brayner, Ângelo
Casanova, Marco
Pequeno, Valéria
Franco, Wellington
Keywords: Quality Assessment
Linked Data Mashup
Fuzzy Inference System
Data Quality
Logic Fuzzy
Issue Date: May-2019
Publisher: SciTePress
Abstract: For several applications, an integrated view of linked data, denoted linked data mashup, is a critical requirement. Nonetheless, the quality of linked data mashups highly depends on the quality of the data sources. In this sense, it is essential to analyze data source quality and to make this information explicit to consumers of such data. This paper introduces a fuzzy ontology to represent the quality of linked data source. Furthermore, the paper shows the applicability of the fuzzy ontology in the process of evaluating data source quality used to build linked data mashups.
Peer reviewed: yes
URI: http://hdl.handle.net/11144/4315
metadata.dc.identifier.doi: 10.5220/0007718803990406
ISBN: 978-989-758-372-8
Appears in Collections:AUTONOMA TECHLAB - Artigos/Papers

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