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Detecting incorrect numerical data in DBpedia

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2014) 8465 LNCS 504-518
DOI: 10.1007/978-3-319-07443-6_34
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Abstract

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 different semantic grouping methods. Our approach reaches 87% precision, and has lead to the identification of 11 systematic errors in the DBpedia extraction framework. © 2014 Springer International Publishing.

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Wienand, D., & Paulheim, H. (2014). Detecting incorrect numerical data in DBpedia. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8465 LNCS, pp. 504–518). Springer Verlag. https://doi.org/10.1007/978-3-319-07443-6_34

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