Fuzzy Knowledge Representation, Learning and Optimization with Bayesian Analysis in Fuzzy Semantic Networks

摘要

This paper presents a method of optimization, based on both Bayesian Analysistechnical and Gallois Lattice, of a Fuzzy Semantic Networks. The technicalSystem we use learn by interpreting an unknown word using the links createdbetween this new word and known words. The main link is provided by the contextof the query. When novice's query is confused with an unknown verb (goal)applied to a known noun denoting either an object in the ideal user's Networkor an object in the user's Network, the system infer that this new verbcorresponds to one of the known goal. With the learning of new words in naturallanguage as the interpretation, which was produced in agreement with the user,the system improves its representation scheme at each experiment with a newuser and, in addition, takes advantage of previous discussions with users. Thesemantic Net of user objects thus obtained by these kinds of learning is notalways optimal because some relationships between couple of user objects can begeneralized and others suppressed according to values of forces thatcharacterize them. Indeed, to simplify the obtained Net, we propose to proceedto 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 theobtained descriptive graph.