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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).
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 ...
In the case of document retrieval, queries can be based on full-text or other content-based indexing. Information retrieval is the science [ 1 ] of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.
Many search engines incorporate an inverted index when evaluating a search query to quickly locate documents containing the words in a query and then rank these documents by relevance. Because the inverted index stores a list of the documents containing each word, the search engine can use direct access to find the documents associated with ...
Like a word or phrase search stemming and fuzzy searches can apply. A word input can be put in double "quotes" to turn off stemming. A phrase input can use greyspace to turn on stemming. A single word input can suffix the tilde ~ character for a fuzzy search. A single word input can suffix the star * character for a wildcard search.
Searching: Searching finds documents and folders using template attributes or full text search. Documents can be searched using various attributes and document content. Federated search: This refers to the capability to extend search capabilities to draw results from multiple sources, or from multiple DMSes within an enterprise. [14] Publishing
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.
In information retrieval, Okapi BM25 (BM is an abbreviation of best matching) is a ranking function used by search engines to estimate the relevance of documents to a given search query. It is based on the probabilistic retrieval framework developed in the 1970s and 1980s by Stephen E. Robertson, Karen Spärck Jones, and others.