DL-Learner 1.1

  • Preliminary support for XSD date/time literals
  • Preliminary support for inverse properties
  • Better support for SPARQL endpoints as knowledge sources
  • New Query Tree Learning (QTL) algorithms
  • Optional DL syntax output instead of Manchester
  • Documentation updates
  • Bug fixes

DL-Learner 1.0

  • Project migrated to Github
  • All internal data structures migrated to OWL API
  • Protégé plugin moved to a separate project
  • Several improvements in query tree learner for robustness against noisy input
  • Experimental Punning Handling
  • New Utilities
    • Concise Bounded Description generator
    • Jena-OWLAPI converter

DL-Learner 1.0 Beta-3 (2014-05-23)

DL-Learner 1.0 Beta-2 (2012-04-06)

  • Removed dependencies to Sesame and several updated dependencies including Jena
  • Thread safety improvements and parallelism in several algorithms and processing steps
  • Lightweight REST interface

DL-Learner 1.0 Beta-1 (2011-12-08)

  • Added stochastic sampling for cases with many input examples to further algorithms
  • Modification of conf file format and improved compatibility for many algorithms
  • Significant work on using DL-Learner for feedback loops in question answering
  • Many bug fixes

DL-Learner 1.0 Alpha-2 (2011-09-15)

  • Bugfix release
  • OWL API communication and command line interface issues resolved

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 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 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 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.