- From: Jens Lehmann <jens.lehmann@cs.uni-bonn.de>
- Date: Tue, 11 Oct 2016 21:51:17 +0200
- To: public-lod@w3.org
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 19:51:44 UTC