Uncertain and Approximative Knowledge Representation to Reasoning on
  Classification with a Fuzzy Networks Based System
  The approach described here allows to use the fuzzy Object Based
Representation of imprecise and uncertain knowledge. This representation has a
great practical interest due to the possibility to realize reasoning on
classification with a fuzzy semantic network based system. For instance, the
distinction between necessary, possible and user classes allows to take into
account exceptions that may appear on fuzzy knowledge-base and facilitates
integration of user's Objects in the base. This approach describes the
theoretical aspects of the architecture of the whole experimental A.I. system
we built in order to provide effective on-line assistance to users of new
technological systems: the understanding of "how it works" and "how to complete
tasks" from queries in quite natural languages. In our model, procedural
semantic networks are used to describe the knowledge of an "ideal" expert while
fuzzy sets are used both to describe the approximative and uncertain knowledge
of novice users in fuzzy semantic networks which intervene to match fuzzy
labels of a query with categories from our "ideal" expert.
