DL-Learner Tool for supervised Machine Learning in OWL and Description Logics
Download

DL-Learner Ranking & Summary

DL-Learner Tags

learn description description logistics learn classes learn dl logistic

DL-Learner Description

Tool for supervised Machine Learning in OWL and Description Logics The DL-Learner software learns concepts in Description Logics (DLs) from user-provided examples. Equivalently, it can be used to learn classes in OWL ontologies from selected objects. DL-Learner extends Inductive Logic Programming to Descriptions Logics and the Semantic Web.The goal of DL-Learner is to provide a DL/OWL based machine learning tool to support knowledge engineers in constructing knowledge and solve supervised learnings tasks and learning about the data they created. Here are some key features of "DL-Learner": implements different algorithms: · a refinement operator based algorithm · 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 · KAON2: direct Java API access (may be removed in the future, because KAON2 is not open source) different user interfaces: · command line · web service · planned: PHP client · planned: 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 What's New in This Release: · 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

DL-Learner Related Software