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One of the most difficult problems in the development of intelligent systemsis the construction of the underlying knowledge base. As a consequence, therate of progress in the development of this type of system is directly relatedto the speed with which knowledge bases can be assembled, and on its quality.We attempt to solve the knowledge acquisition problem, for a BusinessInformation System, developing a supervised multistrategy learning paradigm.This paradigm is centred on a collaborative data mining strategy, where groupsof experts collaborate using data-mining process on the supervised acquisitionof new knowledge extracted from heterogeneous machine learning data models.  The Actias system is our approach to this paradigm. It is the result ofapplying the graphic logic based language of sketches to knowledge integration.The system is a data mining collaborative workplace, where the InformationSystem knowledge base is an algebraic structure. It results from theintegration of background knowledge with new insights extracted from datamodels, generated for specific data modelling tasks, and represented as rulesusing the sketches language.

The Actias system: supervised multi-strategy learning paradigm using categorical logic