Editing Information retrieval (section)

== Overview ==
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. Instead, several objects may match the query, perhaps with different degrees of [[Relevance (information retrieval)|relevance]].

An object is an entity that is represented by information in a content collection or [[database]]. User queries are matched against the database information. However, as opposed to classical SQL queries of a database, in information retrieval the results returned may or may not match the query, so results are typically ranked. This [[ranking (information retrieval)|ranking]] of results is a key difference of information retrieval searching compared to database searching.<ref>Jansen, B. J. and Rieh, S. (2010) [https://faculty.ist.psu.edu/jjansen/academic/jansen_theoretical_constructs.pdf The Seventeen Theoretical Constructs of Information Searching and Information Retrieval] {{Webarchive|url=https://web.archive.org/web/20160304195424/https://faculty.ist.psu.edu/jjansen/academic/jansen_theoretical_constructs.pdf |date=2016-03-04 }}. Journal of the American Society for Information Sciences and Technology. 61(8), 1517–1534.</ref>

Depending on the [[Information retrieval applications|application]] the data objects may be, for example, text documents, images,<ref name=goodron2000>{{cite journal |first=Abby A. |last=Goodrum |title=Image Information Retrieval: An Overview of Current Research |journal=Informing Science |volume=3 |number=2 |year=2000 }}</ref> audio,<ref name=Foote99>{{cite journal |first=Jonathan |last=Foote |title=An overview of audio information retrieval |journal=Multimedia Systems |volume=7 |pages=2–10 |year=1999 |doi=10.1007/s005300050106 |citeseerx=10.1.1.39.6339 |s2cid=2000641 }}</ref> [[mind maps]]<ref name=Beel2009>{{cite conference |first1=Jöran |last1=Beel |first2=Bela |last2=Gipp |first3=Jan-Olaf |last3=Stiller |title=Information Retrieval On Mind Maps - What Could It Be Good For? |url=http://www.sciplore.org/publications_en.php |conference=Proceedings of the 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom'09) |year=2009 |publisher=IEEE |place=Washington, DC |access-date=2012-03-13 |archive-url=https://web.archive.org/web/20110513214422/http://www.sciplore.org/publications_en.php |archive-date=2011-05-13 |url-status=dead }}</ref> or videos. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates or [[metadata]].

Most IR systems compute a numeric score on how well each object in the database matches the query, and rank the objects according to this value. The top ranking objects are then shown to the user. The process may then be iterated if the user wishes to refine the query.<ref name="Frakes1992">{{cite book |last1=Frakes |first1=William B.|last2=Baeza-Yates|first2=Ricardo |title=Information Retrieval Data Structures & Algorithms |publisher=Prentice-Hall, Inc. |year=1992 |isbn=978-0-13-463837-9 |url=https://www.scribd.com/doc/13742235/Information-Retrieval-Data-Structures-Algorithms-William-B-Frakes |url-status=dead |archive-url=https://web.archive.org/web/20130928060217/http://www.scribd.com/doc/13742235/Information-Retrieval-Data-Structures-Algorithms-William-B-Frakes |archive-date=2013-09-28 }}</ref>