23. A system for knowledge discovery from a set of free-text documents, the system comprising: means for extracting semi-structured meta-data from the set of free-text documents; means for filtering the semi-structured meta-data to identify a set of key concepts and a corresponding set of key relations between the key concepts, the set of key concepts corresponding to the plurality of concepts; means for deriving at least one set of sub-concepts corresponding to the set of key concepts based upon data within a domain knowledge base, means for formulating training samples, each training sample including a vector representing a sub-concept and a vector representing a key concept; and means for deriving a set of associations between a set of vectors representing a sub-concept and at least one vector representing a key concept, wherein neither the set of text documents nor the semi-structured meta-data mention the set of associations, and wherein the set of associations corresponds to discovered knowledge that is extractable by a knowledge interpreter.
29. The system according to claim 23 wherein the domain knowledge base comprises a taxonomy, a concept hierarchy network, an ontology, a thesaurus, a relational database, an object-oriented database, or any combination thereof.