Detecting Incorrect Numerical Data in DBpedia
published: July 30, 2014, recorded: May 2014, views: 60
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DBpedia is a central hub of Linked Open Data (LOD). Being based on crowd-sourced contents and heuristic extraction methods, it is not free of errors. In this paper, we study the application of unsupervised numerical outlier detection methods to DBpedia, using Interquantile Range (IQR), Kernel Density Estimation (KDE), and various dispersion estimators, combined with dierent semantic grouping methods. Our approach reaches 87% precision, and has lead to the identication of 11 systematic errors in the DBpedia extraction framework
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