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Quality Assessment of Linked Datasets Using Probabilistic Approximation

  • Jeremy DebattistaEmail author
  • Santiago Londoño
  • Christoph Lange
  • Sören Auer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9088)

Abstract

With the increasing application of Linked Open Data, assessing the quality of datasets by computing quality metrics becomes an issue of crucial importance. For large and evolving datasets, an exact, deterministic computation of the quality metrics is too time consuming or expensive. We employ probabilistic techniques such as Reservoir Sampling, Bloom Filters and Clustering Coefficient estimation for implementing a broad set of data quality metrics in an approximate but sufficiently accurate way. Our implementation is integrated in the comprehensive data quality assessment framework Luzzu. We evaluated its performance and accuracy on Linked Open Datasets of broad relevance.

Keywords

Data quality Linked data Probabilistic approximation 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jeremy Debattista
    • 1
    Email author
  • Santiago Londoño
    • 1
  • Christoph Lange
    • 1
  • Sören Auer
    • 1
  1. 1.University of Bonn and Fraunhofer IAISBonnGermany

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