Softening Fuzzy Knowledge Representation Tool with the Learning of New
  Words in Natural Language
  The approach described here allows using membership function to represent
imprecise and uncertain knowledge by learning in Fuzzy Semantic Networks. This
representation has a great practical interest due to the possibility to realize
on the one hand, the construction of this membership function from a simple
value expressing the degree of interpretation of an Object or a Goal as
compared to an other and on the other hand, the adjustment of the membership
function during the apprenticeship. We show, how to use these membership
functions to represent the interpretation of an Object (respectively of a Goal)
user as compared to an system Object (respectively to a Goal). We also show the
possibility to make decision for each representation of an user Object compared
to a system Object. This decision is taken by determining decision coefficient
calculates according to the nucleus of the membership function of the user
Object.
