Information retrieval

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Information retrieval (IR) is the science of searching for information in documents, searching for documents themselves, searching for metadata which describe documents, or searching within databases, whether relational stand-alone databases or hypertextually-networked databases such as the World Wide Web. There is a common confusion, however, between data retrieval, document retrieval, information retrieval, and text retrieval, and each of these has its own bodies of literature, theory, praxis and technologies. IR is, like most nascent fields, interdisciplinary, based on computer science, mathematics, library science, information science, cognitive psychology, linguistics, statistics, physics.

Automated IR systems are used to reduce information overload. Many universities and public libraries use IR systems to provide access to books, journals, and other documents. IR systems are often related to object and query. Queries are formal statements of information needs that are put to an IR system by the user. An object is an entity which keeps or stores information in a database. User queries are matched to objects stored in the database. A document is, therefore, a data object. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates.

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 such a huge evaluation of text retrieval methodologies.

Web search engines such as Google, Live.com, or Yahoo search are the most visible IR applications.

Contents

[edit] Performance measures

There are several measures on the performance of an information retrieval system. The measures rely on a collection of documents and a query for which the relevancy of the documents is known. All common measures described here assume binary relevancy: the document is either relevant or completely irrelevant. In practice queries may be ill-posed and there may be different shades of relevancy. The formulas for precision, recall and fall-out are translated from the German Wikipedia-article "Recall und Precision". See also this nice intuitive, graphical depiction.

[edit] Precision

The proportion of retrieved and relevant documents to all the documents retrieved:

\mbox{precision}=\frac{|\{\mbox{relevant documents}\}\cap\{\mbox{retrieved documents}\}|}{|\{\mbox{retrieved documents}\}|}

In binary classification, precision is analogous to positive predictive value. Precision takes all retrieved documents into account. It can also be evaluated at a given cut-off rank, considering only the topmost results returned by the system. This measure is called precision at n or P@n.

Note that the meaning and usage of "precision" in the field of Information Retrieval differs from the definition of accuracy and precision within other branches of science and technology.

[edit] Recall

The proportion of relevant documents that are retrieved, out of all relevant documents available:

\mbox{recall}=\frac{|\{\mbox{relevant documents}\}\cap\{\mbox{retrieved documents}\}|}{|\{\mbox{relevant documents}\}|}

In binary classification, recall is called sensitivity.

It is trivial to achieve recall of 100% by returning all documents in response to any query. Therefore recall alone is not enough but one needs to measure the number of irrelevant document also, for example by computing the precision.

[edit] Fall-Out

The proportion of irrelevant documents that are retrieved, out of all irrelevant documents available:

\mbox{fall-out}=\frac{|\{\mbox{irrelevant documents}\}\cap\{\mbox{retrieved documents}\}|}{|\{\mbox{irrelevant documents}\}|}

[edit] F-measure

The weighted harmonic mean of precision and recall, the traditional F-measure or balanced F-score is:

F = 2 \cdot (\mathrm{precision} \cdot \mathrm{recall}) / (\mathrm{precision} + \mathrm{recall}).\,

This is also known as the F1 measure, because recall and precision are evenly weighted.

The general formula for non-negative real α is:

F_\alpha = (1 + \alpha) \cdot (\mathrm{precision} \cdot \mathrm{recall}) / (\alpha \cdot \mathrm{precision} + \mathrm{recall}).\,

Two other commonly used F measures are the F2 measure, which weights recall twice as much as precision, and the F0.5 measure, which weights precision twice as much as recall.

[edit] Average precision

The precision and recall are based on the whole list of documents returned by the system. Average precision emphasizes returning more relevant documents earlier. It is average of precisions computed after truncating the list after each of the relevant documents in turn:

\operatorname{Ave}P = \frac{\sum_{r=1}^N (P(r) \times \mathrm{rel}(r))}{\mbox{number of relevant documents}} \!,

where r is the rank, N the number retrieved, rel() a binary function on the relevance of a given rank, and P() precision at a given cut-off rank.

If there are several queries with known relevancies available, the mean average precision is the mean value of the average precisions computed for each of the queries separately.

[edit] Model types

categorization of IR-models (translated from German entry, original source Dominik Kuropka)
categorization of IR-models (translated from German entry, original source Dominik Kuropka)

For successful IR, it is necessary to represent the documents in some way. There are a number of models for this purpose. They can be categorized according to two dimensions like shown in the figure on the right: the mathematical basis and the properties of the model. (translated from German entry, original source Dominik Kuropka)

[edit] First dimension: mathematical basis

  • Set-theoretic Models represent documents by sets. Similarities are usually derived from set-theoretic operations on those sets. Common models are:
  • Algebraic Models represent documents and queries usually as vectors, matrices or tuples. Those vectors, matrices or tuples are transformed by the use of a finite number of algebraic operations to a one-dimensional similarity measurement.

[edit] Second dimension: properties of the model

  • Models without term-interdependencies treat different terms/words as not interdependent. 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 relay an external source for the degree of interdependency between two terms. (For example a human or sophisticated algorithms.)

[edit] Major Events in the History of Information Retrieval (in the United States)

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 1940's: The U.S. 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.

1950's: Growing concern in the US for a "science gap" with the Soviets 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.

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 1960's: 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 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-1960's: National Library of Medicine developed MEDLARS Medical Literature Analysis and Retrieval System, first major machine-readable database and batch retrieval system

Mid-1960's: 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 1960's: F. W. Lancaster completed evaluation studies of the MEDLARS system and published the first edition of his text on information retrieval

Early 1970's: first online systems--NLM's AIM-TWX, MEDLINE; Lockheed's Dialog; SDC's ORBIT

Early 1970's: 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-1980's: 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, R. 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 Retrieval with emphasis on visualization and multi-reference point systems.

Late 1990's: Web search engine implementation of many features formerly found only in experimental IR systems

[edit] Open source information retrieval systems

[edit] Other retrieval tools

[edit] Major Information retrieval research groups

[edit] Major figures in information retrieval

[edit] Other figures associated with information retrieval

Awards in this field: Tony Kent Strix award.

[edit] ACM SIGIR Gerard Salton Award

1983 - Gerard Salton, Cornell University 
"About the future of automatic information retrieval"
1988 - Karen Spärck Jones, University of Cambridge 
"A look back and a look forward"
1991 - Cyril Cleverdon, Cranfield Institute of Technology 
"The significance of the Cranfield tests on index languages"
1994 - William S. Cooper, University of California, Berkeley 
"The formalism of probability theory in IR: a foundation or an encumbrance?"
1997 - Tefko Saracevic, Rutgers University 
"Users lost: reflections on the past, future, and limits of information science"
2000 - Stephen E. Robertson, City University, London 
"On theoretical argument in information retrieval"
2003 - W. Bruce Croft, University of Massachusetts, Amherst 
"Information retrieval and computer science: an evolving relationship"
2006 - C. J. van Rijsbergen, University of Glasgow, UK 
"Quantum haystacks"

[edit] See also

[edit] External links

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