Information retrieval

From Wikipedia, the free encyclopedia

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 hypertext networked databases such as the Internet or World Wide Web or intranets, for text, sound, images or data. 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.

1 Performance measures
2 Model types
- 2.1 First dimension: mathematical basis
- 2.2 Second dimension: properties of the model
3 Open source information retrieval systems
4 Other retrieval tools
5 Major Information retrieval research groups
6 Major figures in information retrieval
7 Other figures associated with information retrieval
8 ACM SIGIR Gerard Salton Award
9 See also
10 External links

[edit] Performance measures

There are various ways to measure how well the retrieved information matches (i.e., how well it is relevant to) the intended information: 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 can also be evaluated at a given cut-off rank, denoted P@n, instead of all retrieved documents.

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.

[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 $F 1$ 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 $F 2$ measure, which weights recall twice as much as precision, and the $F 0.5$ measure, which weights precision twice as much as recall.

[edit] 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:

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

This method emphasizes returning more relevant documents earlier.

[edit] Model types

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:
- Standard Boolean model
- Extended Boolean model
- fuzzy retrieval

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.
- Vector space model
- Generalized vector space model
- Topic-based vector space model (literature: [1], [2])
- Extended Boolean model
- Enhanced topic-based vector space model (literature: [3], [4])
- Latent semantic indexing aka latent semantic analysis

Probabilistic Models treat the process of document retrieval as a multistage random experiment. Similarities are thus represented as probabilities. Probabilistic theorems like the Bayes' theorem are often used in these models.
- Binary independence retrieval
- Uncertain inference
- Language models
- Divergence from randomness models

[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 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.)

[edit] Open source information retrieval systems

mnoGoSearch is an advanced open source search engine written in C, it can index web multilingual sites (Asian languages too as Chinese, Japanese, Korean) and many databases types (MySQL, PostgreSQL, Oracle...).
ht://dig Open source web crawling software
Glimpse and Webglimpse advanced site search software
Egothor high-performance, full-featured text search engine written entirely in Java
Lemur Language Modelling IR Toolkit
Lucene [5] 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 TERabyte RetrIEveR, Information Retrieval Platform, written in Java
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, compact and fast search engine written in C, able to handle large amounts of text

[edit] Other retrieval tools

ASPseek
iHOP Information retrieval system for the biomedical domain
EBIMed Information retrieval (and extraction) system over Medline
DataparkSearch, search engine written in C
Fluid Dynamics Search Engine (FDSE) An open source search engine written in Perl, freeware and shareware versions are available
GalaTex XQuery Full-Text Search (XML query text search)
Information Storage and Retrieval Using Mumps (Online GPL Text)
Sphinx Free open-source SQL full-text search engine

[edit] Major Information retrieval research groups

[edit] Major figures in information retrieval

Gerard Salton
Hans Peter Luhn
W. Bruce Croft
Karen Spärck Jones
C. J. van Rijsbergen
Donald Kraft
Stephen E. Robertson
Abraham Bookstein
S. Dominich
Stephen P Harter
David Blair

[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 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"
2006 - C. J. van Rijsbergen, University of Glasgow, UK: "Quantum haystacks"

[edit] See also

[edit] External links

Retrieved from "http://en.wikipedia.org../../../i/n/f/Information_retrieval.html"

Category: Information retrieval

We provide Linux to the World