  The integration of knowledge extracted from different models described by
domain experts or from models generated by machine learning algorithms is
strongly conditioned by the lack of an appropriated framework to specify and
integrate structures, learning processes, data transformations and data models
or data rules. In this work we extended algebraic specification methods to be
used in this type of framework. This methodology uses graphic structures
similar to Ehresmann's sketches interpreted on a fuzzy set universe. This
approach takes advantages of the sketches ability to integrate data
deterministic and nondeterministic structures. Selecting this strategy we also
try to take advantage on how the graphic languages, used in Category theory in
general and used for sketch definition in particular, are suited to reasoning
about problems, to structural description and to task specification and task
decomposition.
