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.
Heinsohn was one of the first to provide a probabilistic extension of description logics REF .
Probabilistic Description Logics
ArXiv
ArXiv
Computer Science