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.
