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The approach described here allows using membership function to representimprecise and uncertain knowledge by learning in Fuzzy Semantic Networks. Thisrepresentation has a great practical interest due to the possibility to realizeon the one hand, the construction of this membership function from a simplevalue expressing the degree of interpretation of an Object or a Goal ascompared to an other and on the other hand, the adjustment of the membershipfunction during the apprenticeship. We show, how to use these membershipfunctions 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 thepossibility to make decision for each representation of an user Object comparedto a system Object. This decision is taken by determining decision coefficientcalculates according to the nucleus of the membership function of the userObject.

Softening Fuzzy Knowledge Representation Tool with the Learning of New Words in Natural Language