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Authors: Narciso Arruda 1 ; J. Alcântara 1 ; V. Vidal 1 ; Angelo Brayner 1 ; M. Casanova 2 ; V. 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

ISBN: 978-989-758-372-8

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

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, 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. 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},
}

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
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

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