Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Information Retrieval

  • Giambattista AmatiEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_915


Document retrieval; Text retrieval


Information retrieval (IR) deals with the construction of automatic systems that allow users to inquire about textual data of any kind through natural language queries. The retrieved information from IR systems may vary from a ranked list of relevant textual items of any kind, such as full documents or their excerpts, or can be distilled into more elaborated forms, such as document summaries or answers to questions. Information retrieval is an empirical science that studies representation, storage, and access to information and covers a large number of interdisciplinary topics of theoretical computer science including information theory, machine learning, coding theory, probability theory, programming theory, computational semantics, natural language processing, logics, and algebra. From a practical perspective, research on IR includes data representation; storage and retrieval, such as indexing, data encoding, and text...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Annual international SIGIR conference, Proceedings of the ACM Special Interest Group on Information Retrieval Conference. http://www.sigir.org/
  2. 2.
    Chakrabarti S. Mining the Web: discovering knowledge from hypertext data. Amsterdam: Morgan-Kauffman; 2002.Google Scholar
  3. 3.
    Cleverdon C. The cranfield test on index language devices. ASLIB Proc. 1967;19(6):173–92.CrossRefGoogle Scholar
  4. 4.
    Jardine N, van Rijsbergen CJ. The use of hierarchic clustering in information retrieval. Inf Storage Retr. 1971;7(5):217–40.CrossRefGoogle Scholar
  5. 5.
    Luhn H. A statistical approach to mechanized encoding and searching of literary information. IBM J Res Dev. 1957;1(4):309–17.MathSciNetCrossRefGoogle Scholar
  6. 6.
    Mandelbrot B. An informational theory of the statistical structure of language. In: Jackson W, editor. Communication theory, the second London symposium. Butterworth: London; 1953. p. 486–504.Google Scholar
  7. 7.
    Maron M.E. and Kuhns J.L. On relevance, probabilistic indexing and information retrieval. J ACM. 1960;7(3):216–44. http://doi.acm.org/10.1145/321033.321035.CrossRefGoogle Scholar
  8. 8.
    Robertson SE, Sparck-Jones K. Relevance weighting of search terms. J Am Soc Inf Sci. 1976;27(3):129–46.CrossRefGoogle Scholar
  9. 9.
    Salton G, Lesk ME. The SMART automatic document retrieval systems – an illustration. Commun ACM. 1965;8(6):391–8.CrossRefGoogle Scholar
  10. 10.
    Shannon C. A mathematical theory of communication. Bell Syst Tech J. 1948;27(3):379–423 and 623–656.MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Sparck JK. A statistical interpretation of term specificity and its application in retrieval. J Doc. 1972;28(1):11–21.CrossRefGoogle Scholar
  12. 12.
    Van Rijsbergen C. Information retrieval. 2nd ed. London: Butterworths; 1979.zbMATHGoogle Scholar
  13. 13.
    Witten IH, Moffat A, Bell TC. Managing gigabytes. 2nd ed. San Francisco: Morgan Kaufmann; 1999.zbMATHGoogle Scholar
  14. 14.
    Zipf G. Human behavior and the principle of least effort. Reading: Addison-Wesley; 1949.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Fondazione Ugo BordoniRomeItaly

Section editors and affiliations

  • Giambattista Amati
    • 1
  1. 1.Fondazione Ugo BordoniRomeItaly