  On the one hand, classical terminological knowledge representation excludes
the possibility of handling uncertain concept descriptions involving, e.g.,
"usually true" concept properties, generalized quantifiers, or exceptions. On
the other hand, purely numerical approaches for handling uncertainty in general
are unable to consider terminological knowledge. This paper presents the
language ACP 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 ALUP and introduce the probabilistic
formalism that is based on classes of probabilities and is realized by means of
probabilistic constraints. Besides inferring implicitly existent probabilistic
relationships, the constraints guarantee terminological and probabilistic
consistency. Altogether, the new language ALUP applies to domains where both
term descriptions and uncertainty have to be handled.
