Softening Fuzzy Knowledge Representation Tool with the Learning of New Words in Natural Language
This work addresses knowledge representation for AI systems handling natural language, but it appears incremental as it builds on existing fuzzy logic methods.
The paper tackles the problem of representing imprecise knowledge in fuzzy semantic networks by learning membership functions from user interpretations, enabling decision-making based on calculated coefficients.
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.