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

TL;DR

This paper presents a method for representing and adjusting imprecise knowledge in fuzzy semantic networks by learning new words and membership functions, enabling better decision-making in natural language understanding.

Contribution

It introduces a novel approach to learn and adjust membership functions for fuzzy knowledge representation from simple interpretative values in natural language processing.

Findings

Effective representation of uncertain knowledge using membership functions.

Ability to learn and adjust membership functions during training.

Improved decision-making based on fuzzy interpretation of user and system objects.

Abstract

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.