Using Provenance for Quality Assessment and Repair in Linked Open Data

Flouris, G; Roussakis, Y; Poveda-Villalón, M; Mendes, Pablo N. y Fundulaki, I. (2012). Using Provenance for Quality Assessment and Repair in Linked Open Data. En: "11th International Semantic Web Conference", 12 November, 2012, Boston, Estados Unidos.

Descripción

Título: Using Provenance for Quality Assessment and Repair in Linked Open Data
Autor/es:
  • Flouris, G
  • Roussakis, Y
  • Poveda-Villalón, M
  • Mendes, Pablo N.
  • Fundulaki, I.
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 11th International Semantic Web Conference
Fechas del Evento: 12 November, 2012
Lugar del Evento: Boston, Estados Unidos
Título del Libro: Joint Workshop on Knowledge Evolution and Ontology Dynamics
Fecha: Noviembre 2012
Materias:
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Inteligencia Artificial
Grupo Investigación UPM: oeg
Licencias Creative Commons: Ninguna

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Resumen

As the number of data sources publishing their data on the Web of Data is growing, we are experiencing an immense growth of the Linked Open Data cloud. The lack of control on the published sources, which could be untrustworthy or unreliable, along with their dynamic nature that often invalidates links and causes conflicts or other discrepancies, could lead to poor quality data. In order to judge data quality, a number of quality indicators have been proposed, coupled with quality metrics that quantify the “quality level” of a dataset. In addition to the above, some approaches address how to improve the quality of the datasets through a repair process that focuses on how to correct invalidities caused by constraint violations by either removing or adding triples. In this paper we argue that provenance is a critical factor that should be taken into account during repairs to ensure that the most reliable data is kept. Based on this idea, we propose quality metrics that take into account provenance and evaluate their applicability as repair guidelines in a particular data fusion setting.

Más información

ID de Registro: 14477
Identificador DC: http://oa.upm.es/14477/
Identificador OAI: oai:oa.upm.es:14477
Depositado por: Dr Oscar Corcho
Depositado el: 13 Feb 2013 11:06
Ultima Modificación: 21 Abr 2016 14:10
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