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Detecting Incorrect Numerical Data in DBpedia

This video was recorded at 11th Extended Semantic Web Conference (ESWC), Crete 2014. 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

Keywords:: videolectures, ocwc, oec

Disciplines:

Science and Technology / Computer Science

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Material Type:: Presentation
Date Added to MERLOT:: February 8, 2015
Date Modified in MERLOT:: February 8, 2015
Author:: Heiko Paulheim, Institut für Informatik, University of Mannheim
Submitter:: The Open Education Consortium
Primary Audience:: College General Ed, College Lower Division, College Upper Division
Technical Format:: Video

Mobile Compatibility:: Not specified at this time
Language:: English
Cost Involved:: No
Source Code Available:: No
Creative Commons:: This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States