Utilize este identificador para referenciar este registo: http://hdl.handle.net/11144/4315
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dc.contributor.authorArruda, Narciso-
dc.contributor.authorAlcântara, João-
dc.contributor.authorVidal, Vânia-
dc.contributor.authorBrayner, Ângelo-
dc.contributor.authorCasanova, Marco-
dc.contributor.authorPequeno, Valéria-
dc.contributor.authorFranco, Wellington-
dc.date.accessioned2019-09-19T17:18:14Z-
dc.date.available2019-09-19T17:18:14Z-
dc.date.issued2019-05-
dc.identifier.isbn978-989-758-372-8-
dc.identifier.urihttp://hdl.handle.net/11144/4315-
dc.description.abstractFor 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.por
dc.language.isoengpor
dc.publisherSciTePresspor
dc.rightsopenAccesspor
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/por
dc.subjectQuality Assessmentpor
dc.subjectLinked Data Mashuppor
dc.subjectFuzzy Inference Systempor
dc.subjectData Qualitypor
dc.subjectLogic Fuzzypor
dc.titleA Fuzzy Approach for Data Quality Assessment of Linked Datasetspor
dc.typearticlepor
degois.publication.firstPage399por
degois.publication.lastPage406por
degois.publication.locationGréciapor
degois.publication.titleInternational Conference on Enterprise Information Systemspor
degois.publication.volume1por
dc.peerreviewedyespor
dc.identifier.doi10.5220/0007718803990406por
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