DL-Learner 1.0 Alpha-1 (2011-09-03)

• Major refactoring of command line interface and conf files to internally build on Java Spring
• New conf file syntax introduced with much greater flexibility to create and configure objects (the configuration file syntax is internally mapped to Spring XML configuration objects, so it is essentially a more editing friendly variant of Spring XML)
• Wider support for different OWL axiom types in learning algorithms
• Preliminary support for fuzzy description logics
• Query tree learner

Build 2010-08-07

• 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 40)
• Support for direct Pellet 2 integration and reasoners connected via OWLLink
• 24 bugs fixed and 12 feature requests implemented at sourceforge.net bug tracker

Build 2009-05-06

• New algorithm: CELOE (class expression learning for ontology engineering)
• Protégé Plugin based on CELOE
• Wrote a manual 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 fixed

Build 2008-10-13

• Improved refinement operator based learning approach taking domain/range of properties, property hierarchies, disjoint classes into account to structure search space more efficiently
• DL-Learner GUI for loading, saving, and modifying configuration files
• Fast instance checking algorithm reduces the time to test example coverage of class descriptions significantly
• Carcinogenesis Benchmark
• Extraction component: more flexible structure, SPARQL results are converted to OWL on the fly, correct blank node handling Poster Abstract
• More learning examples provided in release
• 12 bugs and 10 feature requests reported at the sourceforge.net tracker fixed

Build 2008-02-18

• 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

Build 2007-08-31

Initial release.