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A fuzzy search process is one that applies search algorithms in a more lenient way than hard search algorithms that only match specific and rigid results. Fuzzy search can be much more effective for some kinds of searches, because although it may turn out less relevant search results, it may also turn out highly relevant search results that would have been screened out by an excessively rigid search algorithm.
For example, in a rigid search, a user may enter a word like “animal.” While the rigid search will only look for instances of “animal,” a fuzzy search will add the plural form, “animals,” or other similar search terms, or may look for results that have been misspelled or differently punctuated.
One common application of fuzzy search is in academic or archive searches where it is important to get results that have less than 100 percent relevance. Because the user is so often searching for a general idea rather than a technical label, a fuzzy search returns that wider field of results from which the human user can pick to determine relevance in context.
Fuzzy searching can also be useful in translation and other instances where words or phrases may not technically match, even though they have high relevance.