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In the recent years, several approaches for machine learning on the Semantic Web have been proposed. However, no extensive comparisons between those approaches have been undertaken, in particular due to a lack of publicly available, acknowledged benchmark datasets. In this paper, we present a collection of 22 benchmark datasets of different sizes. Such a collection of datasets can be used to conduct quantitative performance testing and systematic comparisons of approaches.

A Collection of Benchmark Datasets for Systematic Evaluations of Machine Learning on the Semantic Web