Extracting term units and fact units from existing databases using the Knowledge Discovery Metamodel

The extraction of business vocabulary is one of the main tasks in discovering business knowledge implemented in a software system. In this paper we present a model-driven approach to the extraction of business vocabularies from databases of existing software systems. We describe a transformation framework for obtaining the Knowledge Discovery Metamodel based representation of data structure and define an algorithm for the extraction of candidates for business vocabulary entries (i.e. Term and Fact Units) from the representation. The extracted candidates may be further refined by business analysts and used for the identification of business scenarios and rules in software systems.

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