Fuzzy knowledge representation, learning and optimization with Bayesian analysis in fuzzy semantic networks

The paper presents an optimization method, based on both Bayesian analysis technique and Gallois lattice of a fuzzy semantic network. The technical system we use learns by interpreting an unknown word using the links created between this new word and known words. The main link is provided by the context of the query. When a novice´s query is confused with an unknown verb (goal) applied to a known noun denoting either an object in the ideal user´s network or an object in the user´s network, the system infers that this new verb corresponds to one of the unknown goals. With the learning of new words for natural language interpretation, which is produced in agreement with the user, the system improves its representation scheme at each experiment with a new user and in addition, takes advantage of previous discussions with users. The semantic net of user objects thus obtained by these kinds of learning is not always optimal because some relationships between a couple of user objects can be generalized and others suppressed according to values of forces that characterize them. Indeed, to simplify the obtained net, we propose to proceed to an inductive Bayesian analysis on the net obtained from Gallois lattice. The objective of this analysis can be seen as an operation of filtering of the obtained descriptive graph