Probabilistic Description Logics

On the one hand, class ical terminological knowledge representation excludes the possi­ bility of handling uncertain concept descrip­ tions involving, e.g., "usually true" concept properties, generalized quantifiers, or excep­ tions. On the other hand, purely numer­ ical approaches for handling uncertainty in general are unable to consider terminologi­ cal knowledge. This paper presents the lan­ guage At:CP which is a probabilistic extension of terminological logics and aims at closing the gap between the two areas of research. We present the formal semantics underlying the language At:CP and introduce the prob­ abilistic formalism that is based on class­ es of probabilities and is realized by means of probabilistic constraints. Besides infer­ ing implicitly existent probabilistic relation­ ships, the constraints guarantee terminologi­ cal and probabilistic consistency. Altogether, the new language .AirP applies to domains where both term descriptions and uncertain­ ty have to be handled.