Extending Term Subsumption systems for Uncertainty Management
  A major difficulty in developing and maintaining very large knowledge bases
originates from the variety of forms in which knowledge is made available to
the KB builder. The objective of this research is to bring together two
complementary knowledge representation schemes: term subsumption languages,
which represent and reason about defining characteristics of concepts, and
proximate reasoning models, which deal with uncertain knowledge and data in
expert systems. Previous works in this area have primarily focused on
probabilistic inheritance. In this paper, we address two other important issues
regarding the integration of term subsumption-based systems and approximate
reasoning models. First, we outline a general architecture that specifies the
interactions between the deductive reasoner of a term subsumption system and an
approximate reasoner. Second, we generalize the semantics of terminological
language so that terminological knowledge can be used to make plausible
inferences. The architecture, combined with the generalized semantics, forms
the foundation of a synergistic tight integration of term subsumption systems
and approximate reasoning models.
