we are happy to announce DL-Learner 1.2.
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 tasks within 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 Protégé, which can give suggestions for axioms to add. DL-Learner is part of the Linked Data Stack – a repository for Linked Data management tools.
In the current release, we improved the support for SPARQL endpoints as knowledge sources. You can now directly use a SPARQL endpoint for learning without an OWL reasoner on top of it. Moreover, we extended DL-Learner to also consider dates and inverse properties for learning. Further efforts were made to improve our Query Tree Learning algorithms (those are used to learn SPARQL queries rather than OWL class expressions).
We want to thank everyone who helped to create this release, in particular Robert Höhndorf and Giuseppe Rizzo. 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, Patrick Westphal and Simon Bin