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Authors: Narciso Arruda 1 ; J. Alcântara 1 ; V. M. P. Vidal 1 ; Angelo Brayner 1 ; M. A. Casanova 2 ; V. M. P. Pequeno 3 and Wellington Franco 1

Affiliations: 1 Departamento de Computaç ão, Federal University of Ceará, Fortaleza, Ceará and Brazil ; 2 Department of Informatics, Pontifical Catholic University of Rio de Janeiro and Brazil ; 3 TechLab, Departamento de Ciências e Tecnologias, Universidade Autónoma de Lisboa Luís de Camões and Portugal

Keyword(s): Quality Assessment, Linked Data Mashup, Fuzzy Inference System, Data Quality, Logic Fuzzy.

Related Ontology Subjects/Areas/Topics: Advanced Applications of Fuzzy Logic ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Cloud Computing ; Coupling and Integrating Heterogeneous Data Sources ; Data Engineering ; Databases and Information Systems Integration ; Enterprise Information Systems ; Information Systems Analysis and Specification ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Ontology Engineering ; Semantic Web Technologies ; Services Science ; Software Agents and Internet Computing ; Symbolic Systems

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.

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Paper citation in several formats:
Arruda, N.; Alcântara, J.; Vidal, V.; Brayner, A.; Casanova, M.; Pequeno, V. and Franco, W. (2019). A Fuzzy Approach for Data Quality Assessment of Linked Datasets. In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-372-8; ISSN 2184-4984, SciTePress, pages 399-406. DOI: 10.5220/0007718803990406

@conference{iceis19,
author={Narciso Arruda. and J. Alcântara. and V. M. P. Vidal. and Angelo Brayner. and M. A. Casanova. and V. M. P. Pequeno. and Wellington Franco.},
title={A Fuzzy Approach for Data Quality Assessment of Linked Datasets},
booktitle={Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2019},
pages={399-406},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007718803990406},
isbn={978-989-758-372-8},
issn={2184-4984},
}

TY - CONF

JO - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - A Fuzzy Approach for Data Quality Assessment of Linked Datasets
SN - 978-989-758-372-8
IS - 2184-4984
AU - Arruda, N.
AU - Alcântara, J.
AU - Vidal, V.
AU - Brayner, A.
AU - Casanova, M.
AU - Pequeno, V.
AU - Franco, W.
PY - 2019
SP - 399
EP - 406
DO - 10.5220/0007718803990406
PB - SciTePress

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