Introduction to Probabilistic Ontologies

There is no doubt about it; an accurate representation of a real knowledge domain must be able to capture uncertainty. As the best known formalism for handling uncertainty, probability theory is often called upon this task, giving rise to probabilistic ontologies. Unfortunately, things are not as simple as they might appear, and different choices made can deeply affect the semantics and computational properties of probabilistic ontology languages. In this tutorial, we explore the main design choices available, and the situations in which they may be meaningful or not. We then dive deeper into a specific family of probabilistic ontology languages which can express logical and probabilistic dependencies between axioms.