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 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
ICEIS_2019___Fuzzy_Approach_for_Data_Quality_Assessment.pdf | 262,26 kB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License