1. A method for discovering knowledge from a set of text documents using a processor, the method comprising: extracting semi-structured meta-data from the set of text documents using a meta-data extractor, the semi-structured meta-data comprising a plurality of concepts and a plurality of relations between the concepts; 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; deriving at least one set of sub-concepts corresponding to the set of key concepts based upon data within a domain knowledge base, using a meta-data transformer; formulating a plurality of training samples, each training sample including a vector representing a sub-concept and a vector representing a key concept; and analyzing the plurality of training samples using an associative discoverer to derive 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.
1. A method for discovering knowledge from a set of text documents using a processor, the method comprising: extracting semi-structured meta-data from the set of text documents using a meta-data extractor, the semi-structured meta-data comprising a plurality of concepts and a plurality of relations between the concepts; 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; deriving at least one set of sub-concepts corresponding to the set of key concepts based upon data within a domain knowledge base, using a meta-data transformer; formulating a plurality of training samples, each training sample including a vector representing a sub-concept and a vector representing a key concept; and analyzing the plurality of training samples using an associative discoverer to derive 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. 36. The method of claim 1 wherein filtering the semi-structured meta-data includes automatically performing a statistical analysis upon the semi-structured meta-data.