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DL-Learner 1.3 (Supervised Structured Machine Learning Framework) Released

From: Jens Lehmann <jens.lehmann@cs.uni-bonn.de>
Date: Tue, 11 Oct 2016 21:50:36 +0200
To: semantic-web@w3.org
Message-ID: <bc0369ed-f252-0be5-9a53-628cc128e0dc@cs.uni-bonn.de>

Dear all,

the Smart Data Analytics group [1] is happy to announce DL-Learner 1.3.

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.

Website: http://dl-learner.org
GitHub page: https://github.com/AKSW/DL-Learner
Download: https://github.com/AKSW/DL-Learner/releases
ChangeLog: http://dl-learner.org/development/changelog/

DL-Learner is used for data analysis tasks within other tools such as
ORE [2] and RDFUnit [3]. Technically, it uses refinement operator based,
pattern-based and evolutionary techniques for learning on structured
data. For a practical example, see [4]. DL-Learner also offers a plugin
for Protégé [5], which can give suggestions for axioms to add.

In the current release, we added a large number of new algorithms and
features. For instance, DL-Learner supports terminological decision tree
learning, it integrates the LEAP and EDGE systems as well as the BUNDLE
probabilistic OWL reasoner. We migrated the system to Java 8, Jena 3,
OWL API 4.2 and Spring 4.3. We want to point to some related efforts here:

* A new DL-Learner overview article is available at [6].
* We started a benchmarking framework for supervised machine learning
from structured data (not restricted to RDF/OWL) at [7].
* An article about the SPARQL reasoning component is now available at
http://svn.aksw.org/papers/2016/ECAI_SPARQL_Learner/public.pdf .
* An article about terminological decision tree learning is available at
[8].

We want to thank everyone who helped to create this release, in
particular we want to thank Giuseppe Cota who visited the core developer
team and significantly improved DL-Learner. We also acknowledge support
by the recently SAKE project [9], in which DL-Learner will be applied to
event analysis in manufacturing use cases, as well as Big Data Europe
[10] and HOBBIT [11] projects.

View this announcement on Twitter and the AKSW blog:
  https://twitter.com/dllearner/status/785928687921299457
  http://blog.aksw.org/dl-learner-1-3/

Kind regards,

Lorenz Bühmann, Jens Lehmann, Patrick Westphal and Simon Bin

[1] http://sda.tech
[2] http://ore-tool.net
[3] http://aksw.org/Projects/RDFUnit.html
[4] http://dl-learner.org/community/carcinogenesis/
[5] https://github.com/AKSW/DL-Learner-Protege-Plugin
[6] http://jens-lehmann.org/files/2016/jws_dllearner.pdf and
http://www.sciencedirect.com/science/article/pii/S157082681630018X
[7] https://github.com/AKSW/SML-Bench
[8]
http://dl-learner.org/Resources/Documents/ekaw_terminological_decision_trees.pdf
[9] https://www.sake-projekt.de/en/start/
[10] http://www.big-data-europe.eu
[11] https://project-hobbit.eu/

-- 
Prof. Dr. Jens Lehmann
http://jens-lehmann.org
Computer Science Institute       Knowledge Discovery Department
University of Bonn               Fraunhofer IAIS
http://www.cs.uni-bonn.de        http://www.iais.fraunhofer.de
lehmann@uni-bonn.de              jens.lehmann@iais.fraunhofer.de
Received on Tuesday, 11 October 2016 19:51:10 UTC

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