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Information retrieval

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Information science
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Information retrieval(IR) incomputingandinformation scienceis the task of identifying and retrievinginformation systemresources that are relevant to aninformation need.The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based onfull-textor other content-based indexing. Information retrieval is thescience[1]of searching for information in a document, searching for documents themselves, and also searching for themetadatathat describes data, and fordatabasesof texts, images or sounds.

Automated information retrieval systems are used to reduce what has been calledinformation overload.An IR system is a software system that provides access to books, journals and other documents; it also stores and manages those documents.Web search enginesare the most visible IR applications.

Overview[edit]

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 ofrelevance.

An object is an entity that is represented by information in a content collection ordatabase.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. Thisrankingof results is a key difference of information retrieval searching compared to database searching.[2]

Depending on theapplicationthe data objects may be, for example, text documents, images,[3]audio,[4]mind maps[5]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 ormetadata.

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.[6]

History[edit]

there is... a machine called the Univac... whereby letters and figures are coded as a pattern of magnetic spots on a long steel tape. By this means the text of a document, preceded by its subject code symbol, can be recorded... the machine... automatically selects and types out those references which have been coded in any desired way at a rate of 120 words a minute

— J. E. Holmstrom, 1948

The idea of using computers to search for relevant pieces of information was popularized in the articleAs We May ThinkbyVannevar Bushin 1945.[7]It would appear that Bush was inspired by patents for a 'statistical machine' – filed byEmanuel Goldbergin the 1920s and 1930s – that searched for documents stored on film.[8]The first description of a computer searching for information was described by Holmstrom in 1948,[9]detailing an early mention of theUnivaccomputer. Automated information retrieval systems were introduced in the 1950s: one even featured in the 1957 romantic comedy,Desk Set.In the 1960s, the first large information retrieval research group was formed byGerard Saltonat Cornell. By the 1970s several different retrieval techniques had been shown to perform well on smalltext corporasuch as the Cranfield collection (several thousand documents).[7]Large-scale retrieval systems, such as the Lockheed Dialog system, came into use early in the 1970s.

In 1992, the US Department of Defense along with theNational Institute of Standards and Technology(NIST), cosponsored theText Retrieval Conference(TREC) as part of the TIPSTER text program. The aim of this was to look into the information retrieval community by supplying the infrastructure that was needed for evaluation of text retrieval methodologies on a very large text collection. This catalyzed research on methods thatscaleto huge corpora. The introduction ofweb search engineshas boosted the need for very large scale retrieval systems even further.

Applications[edit]

Areas where information retrieval techniques are employed include (the entries are in alphabetical order within each category):

General applications[edit]

Domain-specific applications[edit]

Other retrieval methods[edit]

Methods/Techniques in which information retrieval techniques are employed include:

Model types[edit]

Categorization of IR-models (translated fromGerman entry,original sourceDominik Kuropka)

In order to effectively retrieve relevant documents by IR strategies, the documents are typically transformed into a suitable representation. Each retrieval strategy incorporates a specific model for its document representation purposes. The picture on the right illustrates the relationship of some common models. In the picture, the models are categorized according to two dimensions: the mathematical basis and the properties of the model.

First dimension: mathematical basis[edit]

Second dimension: properties of the model[edit]

  • Models without term-interdependenciestreat different terms/words as independent. This fact is usually represented in vector space models by theorthogonalityassumption of term vectors or in probabilistic models by anindependencyassumption for term variables.
  • Models with immanent term interdependenciesallow a representation of interdependencies between terms. However the degree of the interdependency between two terms is defined by the model itself. It is usually directly or indirectly derived (e.g. bydimensional reduction) from theco-occurrenceof those terms in the whole set of documents.
  • Models with transcendent term interdependenciesallow a representation of interdependencies between terms, but they do not allege how the interdependency between two terms is defined. They rely on an external source for the degree of interdependency between two terms. (For example, a human or sophisticated algorithms.)

Performance and correctness measures[edit]

Main article:Evaluation measures (information retrieval)

The evaluation of an information retrieval system' is the process of assessing how well a system meets the information needs of its users. In general, measurement considers a collection of documents to be searched and a search query. Traditional evaluation metrics, designed forBoolean retrieval[clarification needed]or top-k retrieval, includeprecision and recall.All measures assume aground truthnotion of relevance: every document is known to be either relevant or non-relevant to a particular query. In practice, queries may beill-posedand there may be different shades of relevance.

Timeline[edit]

  • Before the1900s
    1801:Joseph Marie Jacquardinvents theJacquard loom,the first machine to use punched cards to control a sequence of operations.
    1880s:Herman Hollerithinvents an electro-mechanical data tabulator using punch cards as a machine readable medium.
    1890Hollerithcards,keypunchesandtabulatorsused to process the1890 US Censusdata.
  • 1920s-1930s
    Emanuel Goldbergsubmits patents for his "Statistical Machine", a document search engine that used photoelectric cells and pattern recognition to search the metadata on rolls of microfilmed documents.
  • 1940s–1950s
    late 1940s:The US military confronted problems of indexing and retrieval of wartime scientific research documents captured from Germans.
    1945:Vannevar Bush'sAs We May Thinkappeared inAtlantic Monthly.
    1947:Hans Peter Luhn(research engineer at IBM since 1941) began work on a mechanized punch card-based system for searching chemical compounds.
    1950s:Growing concern in the US for a "science gap" with the USSR motivated, encouraged funding and provided a backdrop for mechanized literature searching systems (Allen Kentet al.) and the invention of thecitation indexbyEugene Garfield.
    1950:The term "information retrieval" was coined byCalvin Mooers.[10]
    1951:Philip Bagley conducted the earliest experiment in computerized document retrieval in a master thesis atMIT.[11]
    1955:Allen Kent joinedCase Western Reserve University,and eventually became associate director of the Center for Documentation and Communications Research. That same year, Kent and colleagues published a paper in American Documentation describing the precision and recall measures as well as detailing a proposed "framework" for evaluating an IR system which included statistical sampling methods for determining the number of relevant documents not retrieved.[12]
    1958:International Conference on Scientific Information Washington DC included consideration of IR systems as a solution to problems identified. See:Proceedings of the International Conference on Scientific Information, 1958(National Academy of Sciences, Washington, DC, 1959)
    1959:Hans Peter Luhnpublished "Auto-encoding of documents for information retrieval".
  • 1960s:
    early 1960s:Gerard Saltonbegan work on IR at Harvard, later moved to Cornell.
    1960:Melvin Earl Maronand John Lary Kuhns[13]published "On relevance, probabilistic indexing, and information retrieval" in the Journal of the ACM 7(3):216–244, July 1960.
    1962:
    • Cyril W. Cleverdonpublished early findings of the Cranfield studies, developing a model for IR system evaluation. See: Cyril W. Cleverdon, "Report on the Testing and Analysis of an Investigation into the Comparative Efficiency of Indexing Systems". Cranfield Collection of Aeronautics, Cranfield, England, 1962.
    • Kent publishedInformation Analysis and Retrieval.
    1963:
    • Weinberg report "Science, Government and Information" gave a full articulation of the idea of a "crisis of scientific information". The report was named after Dr.Alvin Weinberg.
    • Joseph Becker andRobert M. Hayespublished text on information retrieval. Becker, Joseph; Hayes, Robert Mayo.Information storage and retrieval: tools, elements, theories.New York, Wiley (1963).
    1964:
    • Karen Spärck Jonesfinished her thesis at Cambridge,Synonymy and Semantic Classification,and continued work oncomputational linguisticsas it applies to IR.
    • TheNational Bureau of Standardssponsored a symposium titled "Statistical Association Methods for Mechanized Documentation". Several highly significant papers, including G. Salton's first published reference (we believe) to theSMARTsystem.
    mid-1960s:
    • National Library of Medicine developedMEDLARSMedical Literature Analysis and Retrieval System, the first major machine-readable database and batch-retrieval system.
    • Project Intrex at MIT.
    1965:J. C. R. LickliderpublishedLibraries of the Future.
    1966:Don Swansonwas involved in studies at University of Chicago on Requirements for Future Catalogs.
    late 1960s:F. Wilfrid Lancastercompleted evaluation studies of the MEDLARS system and published the first edition of his text on information retrieval.
    1968:
    • Gerard Salton publishedAutomatic Information Organization and Retrieval.
    • John W. Sammon, Jr.'s RADC Tech report "Some Mathematics of Information Storage and Retrieval..." outlined the vector model.
    1969:Sammon's "A nonlinear mapping for data structure analysisArchived2017-08-08 at theWayback Machine"(IEEE Transactions on Computers) was the first proposal for visualization interface to an IR system.
  • 1970s
    early 1970s:
    • First online systems—NLM's AIM-TWX, MEDLINE; Lockheed's Dialog; SDC's ORBIT.
    • Theodor Nelsonpromoting concept ofhypertext,publishedComputer Lib/Dream Machines.
    1971:Nicholas JardineandCornelis J. van Rijsbergenpublished "The use ofhierarchic clusteringin information retrieval ", which articulated the" cluster hypothesis ".[14]
    1975:Three highly influential publications by Salton fully articulated his vector processing framework andterm discriminationmodel:
    • A Theory of Indexing(Society for Industrial and Applied Mathematics)
    • A Theory of Term Importance in Automatic Text Analysis(JASISv. 26)
    • A Vector Space Model for Automatic Indexing(CACM18:11)
    1978:The FirstACMSIGIRconference.
    1979:C. J. van Rijsbergen publishedInformation Retrieval(Butterworths). Heavy emphasis on probabilistic models.
    1979:Tamas Doszkocs implemented the CITEnatural language user interfacefor MEDLINE at the National Library of Medicine. The CITE system supported free form query input, ranked output and relevance feedback.[15]
  • 1980s
    1980:First international ACM SIGIR conference, joint with British Computer Society IR group in Cambridge.
    1982:Nicholas J. Belkin,Robert N. Oddy, and Helen M. Brooks proposed the ASK (Anomalous State of Knowledge) viewpoint for information retrieval. This was an important concept, though their automated analysis tool proved ultimately disappointing.
    1983:Salton (and Michael J. McGill) publishedIntroduction to Modern Information Retrieval(McGraw-Hill), with heavy emphasis on vector models.
    1985:David Blair andBill Maronpublish: An Evaluation of Retrieval Effectiveness for a Full-Text Document-Retrieval System
    mid-1980s:Efforts to develop end-user versions of commercial IR systems.
    1985–1993:Key papers on and experimental systems for visualization interfaces.
    Work byDonald B. Crouch,Robert R. Korfhage,Matthew Chalmers, Anselm Spoerri and others.
    1989:FirstWorld Wide Webproposals byTim Berners-LeeatCERN.
  • 1990s
    1992:FirstTRECconference.
    1997:Publication ofKorfhage'sInformation Storage and Retrieval[16]with emphasis on visualization and multi-reference point systems.
    1999:Publication ofRicardo Baeza-Yatesand Berthier Ribeiro-Neto'sModern Information Retrievalby Addison Wesley, the first book that attempts to cover all IR.
    late 1990s:Web search enginesimplementation of many features formerly found only in experimental IR systems. Search engines become the most common and maybe best instantiation of IR models.

Major conferences[edit]

Awards in the field[edit]

See also[edit]

References[edit]

  1. ^Luk, R. W. P. (2022). "Why is information retrieval a scientific discipline?".Foundations of Science.27(2): 427–453.doi:10.1007/s10699-020-09685-x.hdl:10397/94873.S2CID220506422.
  2. ^Jansen, B. J. and Rieh, S. (2010)The Seventeen Theoretical Constructs of Information Searching and Information RetrievalArchived2016-03-04 at theWayback Machine.Journal of the American Society for Information Sciences and Technology. 61(8), 1517-1534.
  3. ^Goodrum, Abby A. (2000). "Image Information Retrieval: An Overview of Current Research".Informing Science.3(2).
  4. ^Foote, Jonathan (1999). "An overview of audio information retrieval".Multimedia Systems.7:2–10.CiteSeerX10.1.1.39.6339.doi:10.1007/s005300050106.S2CID2000641.
  5. ^Beel, Jöran; Gipp, Bela; Stiller, Jan-Olaf (2009).Information Retrieval On Mind Maps - What Could It Be Good For?.Proceedings of the 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom'09). Washington, DC: IEEE. Archived fromthe originalon 2011-05-13.Retrieved2012-03-13.
  6. ^Frakes, William B.; Baeza-Yates, Ricardo (1992).Information Retrieval Data Structures & Algorithms.Prentice-Hall, Inc.ISBN978-0-13-463837-9.Archived fromthe originalon 2013-09-28.
  7. ^abSinghal, Amit (2001)."Modern Information Retrieval: A Brief Overview"(PDF).Bulletin of the IEEE Computer Society Technical Committee on Data Engineering.24(4): 35–43.
  8. ^Mark Sanderson & W. Bruce Croft (2012)."The History of Information Retrieval Research".Proceedings of the IEEE.100:1444–1451.doi:10.1109/jproc.2012.2189916.
  9. ^JE Holmstrom (1948)."'Section III. Opening Plenary Session ".The Royal Society Scientific Information Conference, 21 June-2 July 1948: Report and Papers Submitted:85.
  10. ^Mooers, Calvin N.;The Theory of Digital Handling of Non-numerical Information and its Implications to Machine Economics(Zator Technical Bulletin No. 48), cited inFairthorne, R. A. (1958)."Automatic Retrieval of Recorded Information".The Computer Journal.1(1): 37.doi:10.1093/comjnl/1.1.36.
  11. ^Doyle, Lauren; Becker, Joseph (1975).Information Retrieval and Processing.Melville. pp. 410 pp.ISBN978-0-471-22151-7.
  12. ^Perry, James W.; Kent, Allen; Berry, Madeline M. (1955). "Machine literature searching X. Machine language; factors underlying its design and development".American Documentation.6(4): 242–254.doi:10.1002/asi.5090060411.
  13. ^Maron, Melvin E. (2008)."An Historical Note on the Origins of Probabilistic Indexing"(PDF).Information Processing and Management.44(2): 971–972.doi:10.1016/j.ipm.2007.02.012.
  14. ^N. Jardine, C.J. van Rijsbergen (December 1971). "The use of hierarchic clustering in information retrieval".Information Storage and Retrieval.7(5): 217–240.doi:10.1016/0020-0271(71)90051-9.
  15. ^Doszkocs, T.E. & Rapp, B.A. (1979). "Searching MEDLINE in English: a Prototype User Interface with Natural Language Query, Ranked Output, and relevance feedback," In: Proceedings of the ASIS Annual Meeting, 16: 131-139.
  16. ^Korfhage, Robert R. (1997).Information Storage and Retrieval.Wiley. pp.368 pp.ISBN978-0-471-14338-3.

Further reading[edit]

  • Yeo, ShinJoung. (2023)Behind the Search Box: Google and the Global Internet Industry(U of Illinois Press, 2023) ISBN 10:0252087127online

External links[edit]

Wikiquote has quotations related toInformation retrieval.
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