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Dl-Learner—A Framework for Inductive Learning on the Semantic Web

11 Pages Posted: 26 Jun 2018 First Look: Accepted

See all articles by Lorenz Bühmann

Lorenz Bühmann

University of Leipzig - Agile Knowledge Engineering and Semantic Web (AKSW)

Jens Lehmann

University of Leipzig - Agile Knowledge Engineering and Semantic Web (AKSW)

Patrick Westphal

University of Leipzig - Agile Knowledge Engineering and Semantic Web (AKSW)

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.

Keywords: System description, Machine learning, Supervised learning, Semantic Web, OWL, RDF

Suggested Citation

Bühmann, Lorenz and Lehmann, Jens and Westphal, Patrick, Dl-Learner—A Framework for Inductive Learning on the Semantic Web (2016). Journal of Web Semantics First Look. Available at SSRN: https://ssrn.com/abstract=3199236 or http://dx.doi.org/10.2139/ssrn.3199236

Lorenz Bühmann (Contact Author)

University of Leipzig - Agile Knowledge Engineering and Semantic Web (AKSW) ( email )

Augustusplatz 10/11
Leipzig, 04109
Germany

Jens Lehmann

University of Leipzig - Agile Knowledge Engineering and Semantic Web (AKSW) ( email )

Augustusplatz 10/11
Leipzig, 04109
Germany

Patrick Westphal

University of Leipzig - Agile Knowledge Engineering and Semantic Web (AKSW) ( email )

Augustusplatz 10/11
Leipzig, 04109
Germany

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