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Most data sets that describe and evolve from real-world systems are by nature semiquantitative or qualitative rather than quantitative. This can mean large variations in the significance of results that are derived from this data for decision-making processes given that the original database provides training and prototypical examples that reflect systems ofevents in the real world. In this article we propose a structure for a KnowledgeBased System (KBS) that is derived using significance within given contextual domains. Data that would ordinarily be classified by simple attribute classification techniques are now categorized by understanding patterns and value distributions for attributes and attribute domains that exist within rich and dense databases such as in the case of census databasest and Geographic Information Systems (GIS)§; rich by the very number of fields and interpretations, depending on the context in which the data are to be reviewed. The structure we have implemented for capturing and structuring semiquantitative information is the Fuzzy Cognitive Map (FCM). We also reduce the number offalse patterns labeled "significant" by incorporating the knowledge used by human experts to find significance within the data. We treat this knowledge as initial background knowledge and as a minimal set for distinguishing significance for particular attribute values within a given context. © 1996 John Wiley & Sons, Inc.

A context-driven intelligent database processing system using object-oriented fuzzy cognitive maps