Fuzzy Knowledge Representation, Learning and Optimization with Bayesian
  Analysis in Fuzzy Semantic Networks
  This paper presents a method of optimization, based on both Bayesian Analysis
technical and Gallois Lattice, of a Fuzzy Semantic Networks. The technical
System we use learn 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 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 infer that this new verb
corresponds to one of the known goal. With the learning of new words in natural
language as the interpretation, which was 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 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.
