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Descripción
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| 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. | |
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Internacional
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Si |
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Nombre congreso
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Joint Workshop on Knowledge Evolution and Ontology Dynamics (EvoDyn 2012) at the 11th International Semantic Web Conference (ISWC2012) |
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Tipo de participación
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960 |
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Lugar del congreso
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Boston, Estados Unidos |
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Revisores
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Si |
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ISBN o ISSN
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1613-0073 |
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DOI
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Fecha inicio congreso
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12/11/2012 |
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Fecha fin congreso
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12/11/2012 |
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Desde la página
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12 |
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Hasta la página
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12 |
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Título de las actas
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Proceedings of the 2nd Joint Workshop on Knowledge Evolution and Ontology Dynamics |