Detecting Errors in Numerical Linked Data using Cross-Checked Outlier Detection
published: Dec. 19, 2014, recorded: October 2014, views: 1871
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.
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.
Download slides: iswc2014_fleischhacker_detecting_errors_01.pdf (1.6 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !