Information retrieval

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استرجاع المعلومات ( IR ) هو عملية الحصول على موارد نظام المعلومات ذات الصلة بالحاجة إلى المعلومات من مجموعة من تلك الموارد. يمكن أن تستند عمليات البحث إلى نص كامل أو فهرسة أخرى قائمة على المحتوى. استرجاع المعلومات هو علم البحث عن المعلومات في مستند ، والبحث عن المستندات نفسها ، وكذلك البحث عن البيانات الوصفية التي تصف البيانات ، وقواعد بيانات النصوص أو الصور أو الأصوات.

تُستخدم أنظمة استرجاع المعلومات الآلية لتقليل ما يسمى بالحمل الزائد للمعلومات . نظام IR هو نظام برمجي يوفر الوصول إلى الكتب والمجلات والمستندات الأخرى ؛ يخزن ويدير تلك المستندات. محركات البحث على الويب هي أكثر تطبيقات الأشعة تحت الحمراء وضوحًا.

نظرة عامة

تبدأ عملية استرداد المعلومات عندما يقوم المستخدم بإدخال استعلام في النظام. الاستعلامات عبارة عن بيانات رسمية للاحتياجات من المعلومات ، على سبيل المثال سلاسل البحث في محركات البحث على الويب. في استرجاع المعلومات ، لا يحدد الاستعلام عنصرًا واحدًا في المجموعة بشكل فريد. بدلاً من ذلك ، قد تتطابق عدة كائنات مع الاستعلام ، ربما بدرجات مختلفة من الصلة .

الكائن هو كيان يتم تمثيله بمعلومات في مجموعة محتوى أو قاعدة بيانات . تتم مطابقة استعلامات المستخدم مع معلومات قاعدة البيانات. ومع ذلك ، على عكس استعلامات SQL الكلاسيكية لقاعدة البيانات ، في استرداد المعلومات ، قد تتطابق النتائج التي تم إرجاعها مع الاستعلام أو قد لا تتطابق معه ، لذلك يتم ترتيب النتائج عادةً. هذا الترتيب للنتائج هو اختلاف رئيسي في البحث عن المعلومات مقارنة بالبحث في قاعدة البيانات. [1]

Depending on the application the data objects may be, for example, text documents, images,[2] audio,[3] mind maps[4] 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.[5]

History

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 article As We May Think by Vannevar Bush in 1945.[6] It would appear that Bush was inspired by patents for a 'statistical machine' - filed by Emanuel Goldberg in the 1920s and '30s - that searched for documents stored on film.[7] The first description of a computer searching for information was described by Holmstrom in 1948,[8] detailing an early mention of the Univac computer. Automated information retrieval systems were introduced in the 1950s: one even featured in the 1957 romantic comedy, Desk Set. في الستينيات من القرن الماضي ، تم تشكيل أول مجموعة بحثية كبيرة لاسترجاع المعلومات بواسطة جيرارد سالتون في جامعة كورنيل. بحلول سبعينيات القرن الماضي ، ثبت أن العديد من تقنيات الاسترجاع المختلفة تعمل بشكل جيد في مجموعات نصية صغيرة مثل مجموعة كرانفيلد (عدة آلاف من الوثائق). [6] تم استخدام أنظمة الاسترجاع واسعة النطاق ، مثل نظام Lockheed Dialog ، في أوائل السبعينيات.

In 1992, the US Department of Defense along with the National Institute of Standards and Technology (NIST), cosponsored the Text 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 that scale to huge corpora. The introduction of web search engines has boosted the need for very large scale retrieval systems even further.

Applications

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

General applications

Domain-specific applications

طرق الاسترجاع الأخرى

تشمل الأساليب / التقنيات التي تستخدم فيها تقنيات استرجاع المعلومات ما يلي:

أنواع النماذج

تصنيف نماذج IR (مترجم من إدخال ألماني ، المصدر الأصلي Dominik Kuropka ).

لاسترداد المستندات ذات الصلة بشكل فعال من خلال استراتيجيات IR ، يتم تحويل المستندات عادةً إلى تمثيل مناسب. تتضمن كل استراتيجية استرجاع نموذجًا محددًا لأغراض تمثيل المستندات الخاصة بها. توضح الصورة الموجودة على اليمين العلاقة بين بعض النماذج الشائعة. في الصورة ، يتم تصنيف النماذج وفقًا لبعدين: الأساس الرياضي وخصائص النموذج.

البعد الأول: الأساس الرياضي

البعد الثاني: خصائص النموذج

  • Models without term-interdependencies treat different terms/words as independent. This fact is usually represented in vector space models by the orthogonality assumption of term vectors or in probabilistic models by an independency assumption for term variables.
  • Models with immanent term interdependencies allow 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. by dimensional reduction) from the co-occurrence of those terms in the whole set of documents.
  • Models with transcendent term interdependencies allow a representation of interdependencies between terms, but they do not allege how the interdependency between two terms is defined. They rely an external source for the degree of interdependency between two terms. (For example, a human or sophisticated algorithms.)

Performance and correctness measures

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 for Boolean retrieval[clarification needed] or top-k retrieval, include precision and recall. All measures assume a ground truth notion of relevancy: every document is known to be either relevant or non-relevant to a particular query. In practice, queries may be ill-posed and there may be different shades of relevancy.

Timeline

  • Before the 1900s
    1801: Joseph Marie Jacquard invents the Jacquard loom, the first machine to use punched cards to control a sequence of operations.
    1880s: Herman Hollerith invents an electro-mechanical data tabulator using punch cards as a machine readable medium.
    1890 Hollerith cards, keypunches and tabulators used to process the 1890 US Census data.
  • 1920s-1930s
    Emanuel Goldberg submits 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's As We May Think appeared in Atlantic 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 Kent et al.) and the invention of the citation index by Eugene Garfield.
    1950 : مصطلح "استرجاع المعلومات" ابتكره كالفين مويرز . [9]
    1951 : أجرى فيليب باجلي أول تجربة في استرجاع المستندات المحوسبة في أطروحة ماجستير في معهد ماساتشوستس للتكنولوجيا . [10]
    1955 : انضم ألين كينت إلى جامعة كيس ويسترن ريزيرف ، وأصبح في النهاية مديرًا مشاركًا لمركز أبحاث التوثيق والاتصالات. في نفس العام ، نشر كينت وزملاؤه ورقة في American Documentation تصف الدقة وتدابير الاسترجاع بالإضافة إلى تفصيل "إطار" مقترح لتقييم نظام IR والذي تضمن طرق أخذ العينات الإحصائية لتحديد عدد المستندات ذات الصلة التي لم يتم استردادها. [11]
    1958 : المؤتمر الدولي للمعلومات العلمية في واشنطن العاصمة أدرج اعتبار أنظمة IR كحل للمشاكل التي تم تحديدها. انظر: وقائع المؤتمر الدولي للمعلومات العلمية ، 1958 (الأكاديمية الوطنية للعلوم ، واشنطن العاصمة ، 1959)
    1959 : نشر هانز بيتر لون "التشفير التلقائي للوثائق لاسترجاع المعلومات".
  • الستينيات :
    أوائل الستينيات : بدأ جيرارد سالتون العمل على IR في جامعة هارفارد ، وانتقل لاحقًا إلى جامعة كورنيل.
    1960: Melvin Earl Maron and John Lary Kuhns[12] published "On relevance, probabilistic indexing, and information retrieval" in the Journal of the ACM 7(3):216–244, July 1960.
    1962:
    • Cyril W. Cleverdon published 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 published Information 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 and Robert M. Hayes published text on information retrieval. Becker, Joseph; Hayes, Robert Mayo. Information storage and retrieval: tools, elements, theories. New York, Wiley (1963).
    1964:
    • Karen Spärck Jones finished her thesis at Cambridge, Synonymy and Semantic Classification, and continued work on computational linguistics as it applies to IR.
    • The National Bureau of Standards sponsored a symposium titled "Statistical Association Methods for Mechanized Documentation." Several highly significant papers, including G. Salton's first published reference (we believe) to the SMART system.
    mid-1960s:
    • National Library of Medicine developed MEDLARS Medical Literature Analysis and Retrieval System, the first major machine-readable database and batch-retrieval system.
    • Project Intrex at MIT.
    1965: J. C. R. Licklider published Libraries of the Future.
    1966: Don Swanson was involved in studies at University of Chicago on Requirements for Future Catalogs.
    late 1960s: F. Wilfrid Lancaster completed evaluation studies of the MEDLARS system and published the first edition of his text on information retrieval.
    1968:
    • Gerard Salton published Automatic 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 analysis" (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 Nelson promoting concept of hypertext, published Computer Lib/Dream Machines.
    1971: Nicholas Jardine and Cornelis J. van Rijsbergen published "The use of hierarchic clustering in information retrieval", which articulated the "cluster hypothesis."[13]
    1975: Three highly influential publications by Salton fully articulated his vector processing framework and term discrimination model:
    • A Theory of Indexing (Society for Industrial and Applied Mathematics)
    • A Theory of Term Importance in Automatic Text Analysis (JASIS v. 26)
    • A Vector Space Model for Automatic Indexing (CACM 18:11)
    1978: The First ACM SIGIR conference.
    1979: C. J. van Rijsbergen published Information Retrieval (Butterworths). Heavy emphasis on probabilistic models.
    1979: Tamas Doszkocs implemented the CITE natural language user interface for MEDLINE at the National Library of Medicine. The CITE system supported free form query input, ranked output and relevance feedback.[14]
  • 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) published Introduction to Modern Information Retrieval (McGraw-Hill), with heavy emphasis on vector models.
    1985: David Blair and Bill Maron publish: 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 by Donald B. Crouch, Robert R. Korfhage, Matthew Chalmers, Anselm Spoerri and others.
    1989: First World Wide Web proposals by Tim Berners-Lee at CERN.
  • 1990s
    1992: First TREC conference.
    1997: Publication of Korfhage's Information Storage and Retrieval[15] with emphasis on visualization and multi-reference point systems.
    1999: Publication of Ricardo Baeza-Yates and Berthier Ribeiro-Neto's Modern Information Retrieval by Addison Wesley, the first book that attempts to cover all IR.
    late 1990s: Web search engines implementation 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

Awards in the field

See also

References

  1. ^ Jansen, B. J. and Rieh, S. (2010) The Seventeen Theoretical Constructs of Information Searching and Information Retrieval Archived 2016-03-04 at the Wayback Machine. Journal of the American Society for Information Sciences and Technology. 61(8), 1517-1534.
  2. ^ Goodrum, Abby A. (2000). "Image Information Retrieval: An Overview of Current Research". Informing Science. 3 (2).
  3. ^ Foote, Jonathan (1999). "An overview of audio information retrieval". Multimedia Systems. 7: 2–10. CiteSeerX 10.1.1.39.6339. doi:10.1007/s005300050106. S2CID 2000641.
  4. ^ 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 from the original on 2011-05-13. Retrieved 2012-03-13.
  5. ^ Frakes, William B.; Baeza-Yates, Ricardo (1992). Information Retrieval Data Structures & Algorithms. Prentice-Hall, Inc. ISBN 978-0-13-463837-9. Archived from the original on 2013-09-28.
  6. ^ a b Singhal, Amit (2001). "Modern Information Retrieval: A Brief Overview" (PDF). Bulletin of the IEEE Computer Society Technical Committee on Data Engineering. 24 (4): 35–43.
  7. ^ 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.
  8. ^ JE Holmstrom (1948). "'Section III. Opening Plenary Session". The Royal Society Scientific Information Conference, 21 June-2 July 1948: Report and Papers Submitted: 85.
  9. ^ Mooers, Calvin N.; The Theory of Digital Handling of Non-numerical Information and its Implications to Machine Economics (Zator Technical Bulletin No. 48), cited in Fairthorne, R. A. (1958). "Automatic Retrieval of Recorded Information". The Computer Journal. 1 (1): 37. doi:10.1093/comjnl/1.1.36.
  10. ^ Doyle, Lauren; Becker, Joseph (1975). Information Retrieval and Processing. Melville. pp. 410 pp. ISBN 978-0-471-22151-7.
  11. ^ 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.
  12. ^ 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.
  13. ^ 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.
  14. ^ Doszkocs, T.E. & Rapp, B.A. (1979). "Searching MEDLINE in English: a Prototype User Inter-face with Natural Language Query, Ranked Output, and relevance feedback," In: Proceedings of the ASIS Annual Meeting, 16: 131-139.
  15. ^ Korfhage, Robert R. (1997). Information Storage and Retrieval. Wiley. pp. 368 pp. ISBN 978-0-471-14338-3.

Further reading

External links