1. A method of optimizing the selection of databases to be interrogated during query searching comprising the steps of: having a plurality of training sets of documents each characterizing a data domain to be searched; interrogating the various databases with a plurality of keyword sets generated from each of the training sets of documents; analyzing documents obtained from the databases by the interrogations to obtain ranking information from those database documents using multiple base learners and a meta learner to rate the applicability of each of the databases to each data domain using the results to weight the databases relative to one another; and enabling the limitation of the interrogations to the most highly rated sources in a given one of the data domains when the search terms fall within that domain.