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The term "information retrieval" was coined by Calvin Mooers in 1948-50.

IR is a broad Interdisciplinary field, that draws on many other disciplines. Indeed, because it is so broad, it is normally poorly understood, being approached typically from only one perspective or another. It stands at the junction of many established fields, and draws upon Cognitive Psychology , information architecture, Information Design , human information behaviour, Linguistics , Semiotics , Information Science , Computer Science and Librarian ship.

Automated information retrieval (IR) systems were originally used to manage information explosion in scientific literature in the last few decades. 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 documents stored in a 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 Engine s such as Google and Lycos are amongst the most visible applications of information retrieval research.


PERFORMANCE MEASURES


There are various ways to measure how well the retrieved information matches the intended information:


Precision


The proportion of Relevant documents to all the documents retrieved:

P = (number of relevant documents retrieved) / (number of documents retrieved)


In Binary Classification , precision is analogous to Positive Predictive Value .
Precision can also be evaluated at a given cut-off rank, denoted ''P@n'', instead of all retrieved documents.


Recall


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

R = (number of relevant documents retrieved) / (number of relevant documents)


In binary classification, recall is called Sensitivity .


F-measure


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

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

This is also known as the F_1 measure, because recall and precision are evenly weighted.

The general formula is:
:F_N = (1 + N^2) imes \mathrm{precision} imes \mathrm{recall} / ((N^2 imes \mathrm{precision}) + \mathrm{recall}).\,

Two other commonly used F measures are the F_{0.5} measure, which weights precision twice as much as recall, and the F_2 measure, which weights recall twice as much as precision.


Mean average precision


Over a set of queries, find the mean of the average precisions, where Average Precision is the average of the precision after each relevant document is retrieved.

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:

: \operatorname{Ave}P = rac{\sum_{r=1}^N (P(r) imes \mathrm{rel}(r))}{\mbox{number of relevant documents}} \!

This method emphasizes returning more relevant documents earlier.


MODEL TYPES


For a successful IR, it is necessary to represent the documents in some way. There are a number of models for this purpose. They can be classified 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 )


First dimension: mathematical basis





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 Model s by an Independency assumption for term veriables.


  • ''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.)



OPEN SOURCE INFORMATION RETRIEVAL SYSTEMS

  • GalaTex XQuery Full-Text Search (XML query text search)

  • ht://dig Open source web crawling software

  • iHOP Information retrieval system for the biomedical domain

  • EBIMed Information retrieval (and extraction) system over Medline

  • [http://www.cs.uni.edu/~okane/source/ISR/isr.html Information Storage and Retrieval Using Mumps](Online GPL Text)

  • Lemur Language Modelling IR Toolkit

  • Lucene {Link without Title} Apache Jakarta project

  • MG full-text retrieval system Now maintained by the Greenstone Digital Library Software Project

  • SMART Early IR engine from Cornell University

  • Terrier Information Retrieval Platform

  • Wumpus multi-user information retrieval system

  • Xapian Open source IR platform based on Muscat

  • Zebra GPL structured text/XML/MARC boolean search IR engine supporting Z39.50 and Web Services

  • Zettair



MAJOR INFORMATION RETRIEVAL RESEARCH GROUPS



MAJOR FIGURES IN INFORMATION RETRIEVAL




ACM SIGIR GERARD SALTON AWARD

; 1983 - Gerard Salton , Cornell University : "About the future of automatic information retrieval"
; 1988 - Karen Sparck 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"


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