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.
Performance measures
There are various ways to measure how well the retrieved information matches the intended information:
Precision
the proportion of relevant documents of all documents retrieved:
- Precision = (number of relevant docuaments retrieved) / (number of retrieved documents)
In binary classification, precision is called specificity .
Recall
the proportion of retrieved documents of all relevant documents available:
- Recall = (number of retrieved documents) / (number of relevant documents)
In binary classification, recall is called sensitivity.
F-Measure
The geometric mean of precision and recall:
- F = 2 * precision * recall / (precision + recall)
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 roughly dividable into three main groups:
Set-theoretic / Boolean Models
- Standard Boolean Model
- Extended Boolean Model
- fuzzy retrieval
Algebraic / Vector Space Models
Probabilistic models
- Binary Independence Retrieval
- Uncertain Inference
- Language Models
- Divergence From Randomness 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 research groups
Major figures in information retrieval
Awards in this field: Tony Kent Strix award
See also
External links
Last updated: 10-15-2005 06:43:29
