we are happy to announce DL-Learner 1.0.
DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. DL-Learner can use various RDF and OWL serialization formats as well as SPARQL endpoints as input, can connect to most popular OWL reasoners and is easily and flexibly configurable. It extends concepts of Inductive Logic Programming and Relational Learning to the Semantic Web in order to allow powerful data analysis.
DL-Learner is used for data analysis in other tools such as ORE and RDFUnit. Technically, it uses refinement operator based, pattern based and evolutionary techniques for learning on structured data. For a practical example, see http://dl-learner.org/community/carcinogenesis/. It also offers a plugin for Protege, which can give suggestions for axioms to add. DL-Learner is part of the Linked Data Stack – a repository for Linked Data management tools.
We want to thank everyone who helped to create this release, in particular (alphabetically) An Tran, Chris Shellenbarger, Christoph Haase, Daniel Fleischhacker, Didier Cherix, Johanna Völker, Konrad Höffner, Robert Höhndorf, Sebastian Hellmann and Simon Bin. We also acknowledge support by the recently started SAKE project, in which DL-Learner will be applied to event analysis in manufacturing use cases, as well as the GeoKnow and Big Data Europe projects where it is part of the respective platforms.
Lorenz Bühmann, Jens Lehmann and Patrick Westphal