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SML-Bench: A benchmarking framework for structured machine learning

: Westphal, P.; Buhmann, L.; Bin, S.; Jabeen, H.; Lehmann, J.


Semantic web 10 (2019), Nr.2, S.231-245
ISSN: 1570-0844
ISSN: 2210-4968
Fraunhofer IAIS ()

The 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.