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DL-Learner 1.2 (Supervised Structured Machine Learning Framework) Released

From: Lorenz Bühmann <buehmann@informatik.uni-leipzig.de>
Date: Wed, 10 Feb 2016 10:04:37 +0100
To: dl-learner-discussion@lists.sourceforge.net, semantic-web@w3.org, public-lod@w3c.org, ML-news@googlegroups.com, aksw-core@informatik.uni-leipzig.de, geoknow@lists.informatik.uni-leipzig.de, bigdataeurope@googlegroups.com, dl@dl.kr.org
Message-ID: <56BAFD25.1000402@informatik.uni-leipzig.de>

Dear all,

the AKSW group [1] is 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.

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. 
DL-Learner is part of the Linked Data Stack [6] - 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 [7] and Big Data Europe [8] projects where it is part of the 
respective platforms.

*View this announcement on Twitter and the AKSW blog:*



Kind regards,


Lorenz Bühmann, Jens Lehmann, Patrick Westphal and Simon Bin

[1] http://aksw.org

[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://stack.linkeddata.org

[7] http://geoknow.eu

[8] http://www.big-data-europe.eu*
Received on Saturday, 13 February 2016 10:46:31 UTC

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