Learning Ontologies with Epistemic Reasoning: The EL Case
This work addresses ontology learning for knowledge representation systems, but it is incremental as it builds on existing learning models and applies them to a specific logic extension.
The paper tackles the problem of learning description logic ontologies from entailments by introducing a new learning model with epistemic membership and example queries, showing that polynomial learnability in this model aligns with Angluin's exact learning model. It applies this framework to EL, providing complexity results for an epistemic extension of EL and transferring known results to the new model.
We investigate the problem of learning description logic ontologies from entailments via queries, using epistemic reasoning. We introduce a new learning model consisting of epistemic membership and example queries and show that polynomial learnability in this model coincides with polynomial learnability in Angluin's exact learning model with membership and equivalence queries. We then instantiate our learning framework to EL and show some complexity results for an epistemic extension of EL where epistemic operators can be applied over the axioms. Finally, we transfer known results for EL ontologies and its fragments to our learning model based on epistemic reasoning.