DL-Learner—A framework for inductive learning on the Semantic Web

Lorenz Buhmann, Jens Lehmann, Patrick Westphal


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.

Full Text: Untitled
Type of Paper: Research Paper
Keywords: System description; Machine learning; Supervised learning; Semantic Web; OWL; RDF
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