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An information retrieval process begins when a user enters a query into the system. Queries are formal statements of information needs, for example search strings in web search engines. In information retrieval, a query does not uniquely identify a single object in the collection.
In text retrieval, full-text search refers to techniques for searching a single computer-stored document or a collection in a full-text database.Full-text search is distinguished from searches based on metadata or on parts of the original texts represented in databases (such as titles, abstracts, selected sections, or bibliographical references).
Contextual search is a form of optimizing web-based search results based on context provided by the user and the computer being used to enter the query. [1] Contextual search services differ from current search engines based on traditional information retrieval that return lists of documents based on their relevance to the query.
The searcher usually has a document that matches a topic or information need. From this document, the searcher is able to find other keywords, descriptors and themes to use in a subsequent search. [2] Citation Pearl Growing is a popular search and retrieval method used by librarians. [3]
Most content based document retrieval systems use an inverted index algorithm. A signature file is a technique that creates a quick and dirty filter, for example a Bloom filter, that will keep all the documents that match to the query and hopefully a few ones that do not. The way this is done is by creating for each file a signature, typically ...
A concept search (or conceptual search) is an automated information retrieval method that is used to search electronically stored unstructured text (for example, digital archives, email, scientific literature, etc.) for information that is conceptually similar to the information provided in a search query.
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 query likelihood model is a language model [1] [2] used in information retrieval.A language model is constructed for each document in the collection. It is then possible to rank each document by the probability of specific documents given a query.