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  4. SML-Bench: A benchmarking framework for structured machine learning
 
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2019
Journal Article
Title

SML-Bench: A benchmarking framework for structured machine learning

Abstract
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.
Author(s)
Westphal, P.
Buhmann, L.
Bin, S.
Jabeen, H.
Lehmann, Jens  
Journal
Semantic web  
DOI
10.3233/SW-180308
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
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