About Ontolearn

Version: ontolearn 0.7.2

GitHub repository: https://github.com/dice-group/Ontolearn

Publisher and maintainer: DICE - data science research group of Paderborn University.

Contact: onto-learn@lists.uni-paderborn.de

License: MIT License

Ontolearn is an open-source software library for explainable structured machine learning in Python.

Ontolearn started with the goal of using Explainable Structured Machine Learning in OWL 2.0 ontologies and this exactly what our library offers. The main contribution are the exclusive concept learning algorithms that are part of this library. Currently, we have 6 fully functioning algorithms that learn concept in description logics. Papers can be found here.

For the base (core) module of Ontolearn we use owlapy which on its end uses Owlready2. Owlapy is a python package based on owlapi (the java counterpart), and implemented by us, the Ontolearn team. For the sake of modularization we have moved it in a separate repository. The modularization aspect helps us to increase readability and reduce complexity. So now we use owlapy not only for OWL 2 entities representation but for ontology manipulation and reasoning as well.

Ontolearn (including owlapy and ontosample) can do the following:

  • Load/save ontologies in RDF/XML, OWL/XML.

  • Modify ontologies by adding/removing axioms.

  • Access individuals/classes/properties of an ontology (and a lot more).

  • Define learning problems.

  • Sample ontologies.

  • Construct class expressions.

  • Use concept learning algorithms to classify positive examples in a learning problem.

  • Use local datasets or datasets that are hosted on a triplestore server, for the learning task.

  • Reason over an ontology.

  • Other convenient functionalities like converting OWL class expressions to SPARQL or DL syntax.

The rest of content after “examples” is build as a top-to-bottom guide, but nevertheless self-containing, where you can learn more in depth about the capabilities of Ontolearn.