Detecting Incorrect Numerical Data in DBpedia
published: July 30, 2014, recorded: May 2014, views: 3464
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !