Identifying Wrong Links between Datasets by Multi-dimensional Outlier Detection
Heiko Paulheim · 2014
Abstract. Links between datasets are an essential ingredient of Linked Open Data. Since the manual creation of links is expensive at large-scale, link sets are often created using heuristics, which may lead to errors. In this paper, we propose an unsupervised approach for finding erroneous links. We represent each link as a feature vector in a higher dimen-sional vector space, and find wrong links by means of different multi-dimensional outlier detection methods. We show how the approach can be implemented in the RapidMiner platform using only off-the-shelf com-ponents, and present a first evaluation with real-world datasets from the Linked Open Data cloud showing promising results, with an F-measure of up to 0.54, and an area under the ROC curve of up to 0.86.