The ORE (Ontology Repair and Enrichment) tool detects inconsistencies, satisfiable classes and likely sources of problems.

ORE uses the DL-Learner framework to suggest definitions and super classes for existing classes in the knowledge base. This works if instance data is available and can be used to detect potential problems and harmonise schema and data in the knowledge base.


  • Detection of inconsistencies and unsatisfiable classes.
  • Efficient computation of problematic axioms (justifications).
  • Fine-grained justifications, e.g. only showing the relevant parts of the axioms to support a minimal semantic repair.
  • Displays impact of selected repair actions.
  • Enrichment of an ontology by learning definitions and super class axioms using machine learning.
  • Guides the user through potential consequences of adding those axioms.
  • Supports debugging as well as enriching very large knowledge bases available as SPARQL endpoints.