Information retrieval

Information retrieval (IR) is the art and science of searching for information in documents, searching for documents themselves, searching for metadata which describes documents, or searching within databases, whether relational stand alone databases or hypertext networked databases such as the Internet 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 have their own bodies of literature, theory, praxis and technologies.

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 librarianship.

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 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 and Lycos are amongst the most visible applications of Information retrieval research.

Model types

For a successful Information Retrieval some kind of a formal representation of documents is needed. There are a number of models for this purpose roughly dividable into three main groups:

Set-theoretic / Boolean Models

Algebraic / Vector Space Models

Vector Space Model
Generalized Vector Space Model
Topic-based Vector Space Model
enhanced Topic-based Vector Space Model
Latent Semantic Indexing aka Latent semantic analysis

Probabilistic models

Open Source information retrieval systems

Lemur Language Modelling IR Toolkit
Terrier Information Retrieval Platform
Xapian Open source IR platform based on Muscat
Zettair

Major information retrieval groups

Major figures in information retrieval

Awards in this field: Tony Kent Strix award

External links