) is the science of searching for documents, for information
within documents and for metadata
about documents, as well as that of searching relational databases
and the World Wide Web
. There is overlap in the usage of the terms data retrieval, document retrieval
, information retrieval, and text retrieval
, but each also has its own body of literature, theory, praxis
and technologies. IR is interdisciplinary
, based on computer science
, library science
, information science
, information architecture
, cognitive psychology
Automated information retrieval systems are used to reduce what has been called "information overload". Many universities and public libraries use IR systems to provide access to books, journals and other documents. Web search engines are the most visible IR applications.
The idea of using computers to search for relevant pieces of information was popularized in an article As We May Think
by Vannevar Bush
in 1945. First implementations of information retrieval systems were introduced in the 1950s and 1960s. By 1990 several different techniques had been shown to perform well on small text corpora (several thousand documents).
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.
The use of digital methods for storing and retrieving information has led to the phenomenon of digital obsolescence, where a digital resource ceases to be readable because the physical media, the reader required to read the media, the hardware, or the software that runs on it, is no longer available. The information is initially easier to retrieve than if it were on paper, but is then effectively lost.
- 1890: Hollerith tabulating machines were used to analyze the US census. (Herman Hollerith).
- 1945: Vannevar Bush's As We May Think appeared in Atlantic Monthly
- Late 1940s: The US military confronted problems of indexing and retrieval of wartime scientific research documents captured from Germans.
- 1947: Hans Peter Luhn (research engineer at IBM since 1941) began work on a mechanized, punch card based system for searching chemical compounds.
- 1950: The term "information retrieval" may have been coined by Calvin Mooers.
- 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 citation indexing (Eugene Garfield).
- 1955: Allen Kent joined Case Western Reserve University, and eventually becomes associate director of the Center for Documentation and Communications Research. That same year, Kent and colleagues publish a paper in American Documentation describing the precision and recall measures, as well as detailing a proposed "framework" for evaluating an IR system, which includes statistical sampling methods for determining the number of relevant documents not retrieved.
- 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 Luhn published "Auto-encoding of documents for information retrieval."
- 1960: Melvin Earl (Bill) Maron and J. L. Kuhns published "On relevance, probabilistic indexing, and information retrieval" in Journal of the ACM 7(3):216-244, July 1960.
- Early 1960s: Gerard Salton began work on IR at Harvard, later moved to Cornell.
- 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 Coll. of Aeronautics, Cranfield, England, 1962.
- 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.
- 1963: 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
- 1964: 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
- Mid-1960s: 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
- 1968: Gerard Salton published Automatic Information Organization and Retrieval.
- 1968: J. W. Sammon'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.
- Late 1960s: F. W. Lancaster completed evaluation studies of the MEDLARS system and published the first edition of his text on information retrieval
- Early 1970s: first online systems--NLM's AIM-TWX, MEDLINE; Lockheed's Dialog; SDC's ORBIT
- Early 1970s: Theodor Nelson promoting concept of hypertext, published Computer Lib/Dream Machines
- 1971: N. Jardine and C. J. Van Rijsbergen published "The use of hierarchic clustering in information retrieval", which articulated the "cluster hypothesis." (Information Storage and Retrieval, 7(5), pp. 217-240, Dec 1971)
- 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.
- 1980: First international ACM SIGIR conference, joint with British Computer Society IR group in Cambridge
- 1982: Belkin, Oddy, and 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 M. McGill) published Introduction to Modern Information Retrieval (McGraw-Hill), with heavy emphasis on vector models.
- 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 D. B. Crouch, Robert R. Korfhage, M. Chalmers, A. Spoerri and others.
- 1989: First World Wide Web proposals by Tim Berners-Lee at CERN.
- 1992: First TREC conference.
- 1997: Publication of Korfhage's Information Storage and Retrieval with emphasis on visualization and multi-reference point systems.
- Late 1990s: Web search engine implementation of many features formerly found only in experimental IR systems
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 relevancy.
An object is an entity which keeps or stores information in a database. User queries are matched to objects stored in the database. Depending on the application the data objects may be, for example, text documents, images 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.
Most IR systems compute a numeric score on how well each object in the database match 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.
Many different measures for evaluating the performance of information retrieval systems have been proposed. The measures require a collection of documents and a query. All common measures described here 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.
Precision is the fraction of the documents retrieved that are relevant to the user's information need.