1. A computer-implemented method for improving performance and quality in computer-implemented semantic searching based on submitted search terms, identifying documents addressing topics suggested by the submitted search terms irrespective of whether the documents contain all or any of the search terms themselves, the searching seeking relevant documents from a corpus of documents having in part homogeneous structured data, the method comprising: a. before accepting any search requests, preselecting a set of common data fields present in the homogeneous structured data of the corpus documents, each data field being preselected for being topic-correlated and language-independent, wherein being language-independent identifies the data field as being in a single, standard natural or artificial language throughout the corpus of documents, regardless of the language of the individual corpus documents, or particular parts thereof, with which the data field is associated; b. operating at the search-controller level to mediate between a request function and an underlying generic text-search system; c. electronically receiving from the request function a query containing a set of search terms; d. performing a narrow expansion of the set of search terms by determining alternative terms to be combined with respective search terms to obtain a more inclusive text-matching query; e. performing a sampling search using the more-inclusive text-matching query obtained in step (d), and sorting the results by relevance to the query, the most relevant results first; f. retrieving a predetermined number of the most relevant results from the search of step (e), the most relevant results constituting a sample set of documents to be analyzed; g. retrieving values of the preselected topic-correlated, language-independent fields associated with the sample set of documents; h. statistically analyzing the distribution of terms in the values of each of the preselected topic-correlated, language-independent fields, retrieved in step (g); i. generating a signature of the sample set of documents as a function of a predetermined-sized portion of the most prevalent terms found in the statistical analysis of step (h), associating with each term a weight, the weight for each term from any single preselected field being monotonically related to the prevalence of the term in its field as observed in the statistical analysis; and j. submitting a new grand query, having the text-matching query devised in step (d) and submitted for the sampling search in step (e), and the signature generated in step (i) based on the results of the sampling search and invoking top values from the preselected fields, as retrieved for the results in step (g), with weights based on the prevalence analysis in step (h).