Detecting Errors in Numerical Linked Data Using Cross-Checked Outlier Detection

Fleischhacker, Daniel ; Paulheim, Heiko ; Bryl, Volha ; Völker, Johanna ; Bizer, Christian

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Document Type: Conference or workshop publication
Year of publication: 2014
Book title: The Semantic Web – ISWC 2014 : 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I
The title of a journal, publication series: Lecture Notes in Computer Science
Volume: 8796
Page range: 357-372
Conference title: ISWC 2014
Location of the conference venue: Riva del Garda, Italy
Date of the conference: October 19-23, 2014
Place of publication: Berlin [u.a.]
Publishing house: Springer
ISBN: 978-3-319-11963-2 , 978-3-319-11964-9
ISSN: 0302-9743 , 1611-3349
Publication language: English
Institution: School of Business Informatics and Mathematics > Web Data Mining (Juniorprofessur) (Paulheim 2013-2017)
School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
School of Business Informatics and Mathematics > Information Systems V: Web-based Systems (Bizer 2012-)
Subject: 004 Computer science, internet
Keywords (English): Linked Data , Data Debugging , Data Quality , Outlier Detection
Abstract: Outlier detection used for identifying wrong values in data is typically applied to single datasets to search them for values of unexpected behavior. In this work, we instead propose an approach which combines the outcomes of two independent outlier detection runs to get a more reliable result and to also prevent problems arising from natural outliers which are exceptional values in the dataset but nevertheless correct. Linked Data is especially suited for the application of such an idea, since it provides large amounts of data enriched with hierarchical information and also contains explicit links between instances. In a first step, we apply outlier detection methods to the property values extracted from a single repository, using a novel approach for splitting the data into relevant subsets. For the second step, we exploit owl:sameAs links for the instances to get additional property values and perform a second outlier detection on these values. Doing so allows us to confirm or reject the assessment of a wrong value. Experiments on the DBpedia and NELL datasets demonstrate the feasibility of our approach.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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