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The basic linguistic assumption of proximity searching is that the proximity of the words in a document implies a relationship between the words. Given that authors of documents try to formulate sentences which contain a single idea, or cluster of related ideas within neighboring sentences or organized into paragraphs, there is an inherent, relatively high, probability within the document ...
The algorithm attempts to find the same word, but in all its word endings. A fuzzy search will match a different word. Words (but not phrases) accept approximate string matching or "fuzzy search". A tilde ~ character is appended for this "sounds like" search. The other word must differ by no more than two letters. Not the first two letters.
In the context of search engines, query expansion involves evaluating a user's input (what words were typed into the search query area, and sometimes other types of data) and expanding the search query to match additional documents. Query expansion involves techniques such as: Finding synonyms of words, and searching for the synonyms as well ...
Ranking of query is one of the fundamental problems in information retrieval (IR), [1] the scientific/engineering discipline behind search engines. [2] Given a query q and a collection D of documents that match the query, the problem is to rank, that is, sort, the documents in D according to some criterion so that the "best" results appear early in the result list displayed to the user.
The closeness of a match is measured in terms of the number of primitive operations necessary to convert the string into an exact match. This number is called the edit distance between the string and the pattern. The usual primitive operations are: [1] insertion: cot → coat; deletion: coat → cot; substitution: coat → cost
In computer science, an algorithm for matching wildcards (also known as globbing) is useful in comparing text strings that may contain wildcard syntax. [1] Common uses of these algorithms include command-line interfaces, e.g. the Bourne shell [2] or Microsoft Windows command-line [3] or text editor or file manager, as well as the interfaces for some search engines [4] and databases. [5]
A simple and inefficient way to see where one string occurs inside another is to check at each index, one by one. First, we see if there is a copy of the needle starting at the first character of the haystack; if not, we look to see if there's a copy of the needle starting at the second character of the haystack, and so forth.
The query criteria of the search is stored in an attribute of the file called "qrystr". [2] When the file is opened, the filesystem indexes were queried and a Tracker window is opened with an up-to-date list of files that match the criteria. Additional features were added to search with subsequent releases.