Features

Features of DL-Learner:

  • implements different algorithms:
    • two refinement operator based algorithms
    • a genetic programming algorithm
    • a hybrid algorithm using genetic refinement operators
    • (random learning, brute force learning)
  • supports different kinds of learning problems:
    • learning concept definitions and inclusion axioms
    • learning from positive and negative examples as well as only from positive examples
  • supports different input formats:
    • OWL files
    • N-triple files
    • internal representation in config files
    • SPARQL endpoints
  • different reasoner adapters:
    • DIG interface: allows all major reasoners
    • OWL API interface (alpha): FaCT++, Pellet
  • different user interfaces:
    • command line
    • web service
    • Java Swing based GUI
  • easily extensible through a component model
    • 4 types of components: knowledge sources, reasoners, learning problems, learning algorithms
    • to implement a new component of one of the above types you only have to extend the correct class in org.dllearner.core and add the name of your file to the components.ini file
  • allows a wide range of configuration options