Evidence suggests that the compromise chosen by description logics is a particularly successful one.
In addition to terminological knowledge, the language utilizes Bayesian networks to express uncertainty about the basic properties of an individual, the number of fillers for its roles, and the properties of these fillers. We show that the complexity of the inference algorithm is the best that can be hoped for in a language that combines description logic with Bayesian networks. In particular, if we restrict to Bayesian networks that support polynomial time inference, the complexity of our inference procedure is also polynomial time.