[DL] DL-Learner 1.0 (Supervised Structured Machine Learning Framework) Released
patrick.westphal at informatik.uni-leipzig.de
Fri Feb 13 11:27:36 CET 2015
the AKSW group  is 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.
GitHub page: https://github.com/AKSW/DL-Learner
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 . DL-Learner 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.
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.
View this announcement on Twitter and the AKSW blog:
Lorenz Bühmann, Jens Lehmann and Patrick Westphal
Department of Computer Science, University of Leipzig
Research Group: http://aksw.org/
More information about the dl