Westphal, P.P.WestphalBuhmann, L.L.BuhmannBin, S.S.BinJabeen, H.H.JabeenLehmann, JensJensLehmann2022-03-052022-03-052019https://publica.fraunhofer.de/handle/publica/25828910.3233/SW-180308The availability of structured data has increased significantly over the past decade and several approaches to learn from structured data have been proposed. These logic-based, inductive learning methods are often conceptually similar, which would allow a comparison among them even if they stem from different research communities. However, so far no efforts were made to define an environment for running learning tasks on a variety of tools, covering multiple knowledge representation languages. With SML-Bench, we propose a benchmarking framework to run inductive learning tools from the ILP and semantic web communities on a selection of learning problems. In this paper, we present the foundations of SML-Bench, discuss the systematic selection of benchmarking datasets and learning problems, and showcase an actual benchmark run on the currently supported tools.en005006629journal article