![]() The string correction algorithm that specifies the differential is the Damerau-Levenshtein distance metric. "Universe" and "inverse", which are semantically different, will match because the syntactic discrepancies are small.Ī match succeeds if the discrepancies are limited to two or fewer edits, where an edit is an inserted, deleted, substituted, or transposed character. In contrast with other queries that analyze the text to handle different forms of the same word ("mice" and "mouse"), the comparisons in a fuzzy query are taken at face value without any linguistic analysis on the text. ![]() Any documents that match on those in the graph are included in results. The engine then returns the topmost relevant matches in the response.įor a term like "university", the graph might have "unversty, universty, university, universe, inverse". The graph consists of up to 50 expansions, or permutations, of each term, capturing both correct and incorrect variants in the process. For example, if your query includes three terms "university of washington", a graph is created for every term in the query search=university~ of~ washington~ (there's no stop-word removal in fuzzy search, so "of" gets a graph). When a fuzzy search is specified, the search engine builds a graph (based on deterministic finite automaton theory) of similarly composed terms, for all whole terms in the query. It's a query expansion exercise that produces a match on terms having a similar composition. Expanding search to cover near-matches has the effect of auto-correcting a typo when the discrepancy is just a few misplaced characters. It does this by scanning for terms having a similar composition. ![]() Azure Cognitive Search supports fuzzy search, a type of query that compensates for typos and misspelled terms in the input string. ![]()
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