We are happy to announce the next release of DL-Learner, a tool for learning OWL class expressions from examples and background knowledge. It extends Inductive Logic Programming (ILP) to Description Logics and the Semantic Web. The tool has matured over the past 3 years and is meanwhile used in a number of applications. Some features of this release are:

• support for OWL API 3 and OWL 2
ORE (ontology repair and enrichment) tool based on DL-Learner algorithms (soon to be migrated to an own project)
• several new heuristics, e.g. generalised F-Measure, and efficient stochastic heuristic approximation methods
• learning algorithms for the EL description logic
• support for hasValue construct in combination with string datatype
• support for refining existing definitions (instead of learning from scratch) for CELOE ontology engineering algorithm
• support for direct Pellet 2 integration and reasoners connected via OWLlink
• more unit tests, bug fixes and features

DL-Learner can be used to:

• solve general supervised Machine Learning problems using ontologies as background knowledge (given as OWL files, SPARQL endpoints, etc.), e.g. it was used to predict whether chemicals can cause cancer
• help knowledge engineers by learning definitions and subclass axioms (see the Protege plugin and OntoWiki plugin)
• generating user recommendations when browsing knowledge bases

I’d like to thank all contributors, in particular active developers and everyone who sent us valuable feedback.

The tool can be be downloaded here.

Today, we released a major update of the DL-Learner plugin for Protege 4, the popular ontology editor. The plugin allows you to get suggestions for axioms you may want to add to your OWL ontology by analysing the instance data using the DL-Learner framework. A screencast is available to show you how it works. The plugin is installable via the standard Protégé plugin mechanism (Protege 4 stable release required). This version allows to fine tune the language features you want to use and displays the length of expressions, which have been searched. It also fixes bugs related to inconsistency handling. More information can be found on the plugin wiki page. Enjoy!

Today, we released DL-Learner Build 2009-05-06. DL-Learner is a tool for learning OWL class expressions from examples and background knowledge. It extends Inductive Logic Programming (ILP) to Description Logics and the Semantic Web. Some notable features in this release are:

• a new learning algorithm (CELOE) designed specifically for extending OWL ontologies
• a Protege plugin using CELOE
• a manual for getting started using DL-Learner
• performance improvements through stochastic methods
• more learning examples, unit tests, code quality improvements

DL-Learner can be used to:

• solve general supervised Machine Learning problems using ontologies as background knowledge (given as OWL files, SPARQL endpoints, etc.), e.g. it was used to predict whether chemicals can cause cancer
• help knowledge engineers by learning definitions and subclass axioms (see the Protege plugin and another one for OntoWiki is in progress)
• generating user recommendations when browsing knowledge bases

I’d like to thank all contributors, in particular active developers and everyone who sent us valuable feedback.

Today, we released a major update of the DL-Learner plugin for Protege 4, the upcoming release of the popular ontology editor. The plugin allows you to get suggestions for axioms you may want to add to your OWL ontology by analysing the instance data using the DL-Learner framework. Again, we prepared a small screencast to give you an idea of how it works. The plugin is installable via the standard Protégé plugin mechanism (Protege 4 build 110 or higher required). This version comes with a completely new suggestion algorithm and enhanced performance. More information can be found on the plugin wiki page. Thanks to Christian Kötteritzsch for his implementation work and Nick Drummond for supporting us in integrating the plugin.