Parameter Learning for Probabilistic Ontologies

Recently, the problem of representing uncertainty in Description Logics (DLs) has received an increasing attention. In probabilistic DLs, axioms contain numeric parameters that are often difficult to specify or to tune for a human. In this paper we present an approach for learning and tuning the parameters of probabilistic ontologies from data. The resulting algorithm, called EDGE, %for Em over bDds for description loGics paramEter learning, is targeted to DLs following the DISPONTE approach, that applies the distribution semantics to DLs.