1. An intelligent search and retrieval method, comprising the steps of: providing a query profiler having a taxonomy database, the taxonomy database including a plurality of taxonomy codes which have explicitly defined contextual relationship and are semantically related; receiving a query from a user; accessing the taxonomy database of the query profiler to identify the taxonomy codes that are relevant to the query; wherein taxonomy codes are identified using a phrase-code frequency-inverse phrase-code document frequency (pcf-ipcdf) score: wherein phrase-code frequency, pcf(p,c), is defined as a number of times a phrase p appears in one or more categorized documents containing a code c; wherein inverse phrase-code document frequency, ipcdf, is defined as the logarithm of: a number of the documents coded with code c, D(c), divided by a number of the documents for which the phrase p and code c appear together, df(p,c); wherein the pcf-ipcdf score, s(p,c), is defined as pcf(p,c) multiplied by ipcdf(p,c); augmenting the query using the taxonomy codes; generating feedback information to the user for query refinement, the feedback information including a plurality of query terms associated with the query and to be selected by the user; presenting the feedback information to the user; receiving one of the query terms from the user; and identifying a source of the query term and presenting to the user; wherein the taxonomy database is generated by: parsing the natural language from the one or more categorized documents; parsing one or more associated taxonomy codes into a data structure; filtering unnecessary or undesirable code elements from the data structure; extracting phrases from the text of the one or more categorized documents; sorting and collating the extracted phrases into a counted phrase list; and mapping the counted phrase list and the one or more associated taxonomy codes into a data table.