I can/should send the code out. The logic is that for any terms in a query that have zero matches, go thru all the terms(!) and calculate the Levenshtein string distance, and return the best matches. A more intelligent way of doing this is to instead look for terms that also match on the 1st "n" (prob 3) chars.
...or prepare in advance a fast lookup index - split all existing terms to bi- or trigrams, create a separate lookup index, and then simply for each term ask a phrase query (phrase = all n-grams from an input term), with a slop > 0, to get similar existing terms. This should be fast, and you could provide a "did you mean" function too...
-- Best regards, Andrzej Bialecki
------------------------------------------------- Software Architect, System Integration Specialist CEN/ISSS EC Workshop, ECIMF project chair EU FP6 E-Commerce Expert/Evaluator ------------------------------------------------- FreeBSD developer (http://www.freebsd.org)
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