Users love fuzzy queries. They assume that these queries will somehow magically find the right combination of proper spellings. Unfortunately, the truth is somewhat more prosaic.
Imagine that we have 1,000 documents containing Schwarzenegger,'' and just
one document with the misspelling Schwarzeneger.'' According to the theory
of term frequency/inverse document frequency, the misspelling is
much more relevant than the correct spelling, because it appears in far fewer
documents!
In other words, if we were to treat fuzzy matches like any other match, we would favor misspellings over correct spellings, which would make for grumpy users.
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Tip
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Fuzzy matching should not be used for scoring purposes—only to widen the net of matching terms in case there are misspellings. |
By default, the match query gives all fuzzy matches the constant score of 1.
This is sufficient to add potential matches onto the end of the result list,
without interfering with the relevance scoring of nonfuzzy queries.
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Tip
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Fuzzy queries alone are much less useful than they initially appear. They are
better used as part of a |