DL-Learner Changelog
What's new in DL-Learner 20100807
Aug 7, 2010- support for OWL API 3
- ORE tool based on DL-Learner algorithms (soon to be migrated to an own project)
- implemented several new heuristics, e.g. generalised F-Measure
- stochastic approximation of computing F-Measure
- 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
- increased number of unit tests (now 32)
- support for direct Pellet 2 integration and reasoners connected via OWLLink
- 17 bugs fixed and 3 feature requests at sourceforge.net trackers (TODO: actually more feature requests have been resolved; sf.net search function does not seem to work)
New in DL-Learner 20090506 (May 12, 2009)
- new algorithm: CELOE (class expression learning for ontology engineering)
- Protégé Plugin based on CELOE
- wrote a PDF Documentmanual for DL-Learner
- an efficient refinement operator for the EL description logic
- fast stochastic class expression coverage estimation included
- reasoner component design and learning problem structure improved
- more learning examples provided and unit tests for ensuring code quality extended
- 6 bugs and feature requests reported at the sourceforge.net tracker fixer
New in DL-Learner 20080218 (Aug 30, 2008)
- Flexible new component based structure: 4 types of components: knowledge sources, reasoners, learning problems, learning algorithms
- easily extensible: to implement a new component of one of the above types you only have to extend the corresponding class in org.dllearner.core and add the name of your class to the components.ini file
- each component can maintain and easily extend its own configuration options
- Support for using SPARQL endpoints as background knowledge, including mechanisms for knowledge fragment selection. This feature enables DL-Learner to use DBpedia as background knowledge.
- Preliminary support for learning from only positive examples and learning of inclusion axioms instead of definitions.
- Support for N-Triple files.
- Support for using role hierarchies in the refinement operator based algorithm.
- Much more powerful web service interface allowing to access and modify all DL-Learner components.
- Reasoners: preliminary OWL API reasoner interface support: Pellet, FaCT++
- KAON2 dropped, such that DL-Learner now depends solely on open source libraries
- A Prolog parser, which can help in converting Prolog files to OWL (thereby transfering ILP problems into OWL learning problems).
- More examples added: complete Moral Reasoner Benchmarks
- more SPARQL benchmarks
- all examples now also available in OWL