Cognitive Maps are abstract knowledge representation framework, suitable to model complex systems. Cognitive Maps are visualized with directed graphs, where nodes represent phenomena and edges represent relationships. Granular Cognitive Maps are augmented Cognitive Maps, which use knowledge granules as information representation model. Conceptually, GCMs originated as an extension of Fuzzy Cognitive Maps. The contribution presented in this paper is a methodology for Granular Cognitive Map reconstruction. The goal of the procedure is to construct a weights matrix - and thereby the GCM, which outputs best describe the phenomena of interest. The article addresses the conflict between generality and specificity of various Granular Cognitive Maps. Balance between generality and specificity is the most important architectural aspect of a model built with knowledge granules. A series of experiments illustrates, how various optimization techniques allow improvement in map's quality without a loss in map's precision.
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