Dear all,
The Smart Data Analytics group [1] and the E.T.-db-MOLE sub-group located at the InfAI Leipzig [2] is happy to announce
DL-Learner 1.4.
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/SmartDataAnalytics/DL-Learner
Download: https://github.com/SmartDataAnalytics/DL-Learner/releases/tag/1.4.0
In the current release, we continued to improve the code and work on our query tree and class expression learning algorithms. The config file can now optionally be written in Json syntax. We updated the packaging to be ready for Java 11 and also tested DL-Learner on Windows. Some logical fixes to the Horizontal Expansion in CELOE were reported and analysed by Yingbing Hua, thanks!
The DL-Learner system has also been presented at The Web Conference in Lyon 2018 [3]. We want to thank everyone who helped to create this release. We also acknowledge support by the following projects: LIMBO [4], QROWD [5], SAKE [6], Big Data Europe [7], HOBBIT [8], GeoKnow [9], GOLD [10], and SLIPO [11].
Kind regards,
Jens Lehmann, Lorenz Bühmann, Patrick Westphal and Simon Bin
[1] http://sda.tech
[2] https://infai.org/efficient-technology-integration/
[3] http://jens-lehmann.org/files/2018/www_dllearner.pdf
[4] https://www.limbo-project.org/
[5] http://qrowd-project.eu/
[6] https://www.sake-projekt.de/
[7] https://www.big-data-europe.eu/
[8] http://project-hobbit.eu/
[9] http://geoknow.eu/
[10] http://aksw.org/Projects/GOLD.html
[11] http://www.slipo.eu/
