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This paper presents a method of optimization, based on both Bayesian Analysistechnical and Galois Lattice of Fuzzy Semantic Network. The technical System weuse learns by interpreting an unknown word using the links created between thisnew word and known words. The main link is provided by the context of thequery. When novice's query is confused with an unknown verb (goal) applied to aknown noun denoting either an object in the ideal user's Network or an objectin the user's Network, the system infer that this new verb corresponds to oneof the known goal. With the learning of new words in natural language as theinterpretation, which was produced in agreement with the user, the systemimproves its representation scheme at each experiment with a new user and, inaddition, takes advantage of previous discussions with users. The semantic Netof user objects thus obtained by learning is not always optimal because somerelationships between couple of user objects can be generalized and otherssuppressed according to values of forces that characterize them. Indeed, tosimplify the obtained Net, we propose to proceed to an Inductive BayesianAnalysis, on the Net obtained from Galois lattice. The objective of thisanalysis can be seen as an operation of filtering of the obtained descriptivegraph.

Optimization of Fuzzy Semantic Networks Based on Galois Lattice and Bayesian Formalism