Presented at: The 12th Extented Semantic Web Conference (ESWC2015)
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 different data quality metrics in an approximate but sufficiently accurate way. Our implementation is integrated in the comprehensive data quality assessment framework Luzzu and evaluated in terms of performance and accuracy on Linked Open Datasets of broad relevance.
Keywords: Probabilistic Approximations, Linked Data, Quality Assessment
Resource URI on the dog food server: http://data.semanticweb.org/conference/eswc/2015/paper/research/125
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