Measure of Similarity between Fuzzy Concepts for Optimization of Fuzzy Semantic Nets
This work addresses optimization in fuzzy semantic networks for users, but it appears incremental as it builds on existing concepts without introducing a major breakthrough.
The paper tackles the problem of minimizing research and identification time for user's objects and goals in semantic networks by measuring similarity between fuzzy concepts, resulting in processing only the most similar objects and goals to avoid analyzing all system elements.
This paper presents a method to measure the similarity between different fuzzy concepts in order to optimize Semantic networks. The problem approached is the minimization of the time of research and identification of user's Objects and Goals. Indeed, it concerns to determine to each instant the totality of Objects (respectively Goals) among which one can identify rapidly the most satisfactory for the user's Object and Goal. Alone Objects and most similar Goals to Objects and researched Goals of the viewpoint of attribute values will be processed, what will avoid the analysis of all Objects and system Goals far of needs of the user.