DL-Learner—A framework for inductive learning on the Semantic Web |
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| Affiliation: | 1. University of Leipzig, Institute of Computer Science, AKSW Group, Augustusplatz 10, D-04009 Leipzig, Germany;2. University of Bonn, Institute of Computer Science, Römerstr. 164, D-53117 Bonn, Germany;1. Dipartimento di Matematica e Informatica, Università della Calabria, Italy;2. Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Finland;3. Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi, Università di Genova, Italy;1. Pontificia Universidad Católica de Chile, Chile;2. Free University of Bozen–Bolzano, Italy |
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| Abstract: | In this system paper, we describe the DL-Learner framework, which supports supervised machine learning using OWL and RDF for background knowledge representation. It can be beneficial in various data and schema analysis tasks with applications in different standard machine learning scenarios, e.g. in the life sciences, as well as Semantic Web specific applications such as ontology learning and enrichment. Since its creation in 2007, it has become the main OWL and RDF-based software framework for supervised structured machine learning and includes several algorithm implementations, usage examples and has applications building on top of the framework. The article gives an overview of the framework with a focus on algorithms and use cases. |
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