Information retrieval

Information retrieval (IR) is the art and 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 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.

数据挖掘工具

The term "information retrieval" was coined by Calvin Mooers in 1948-50. 数据挖掘工具

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.

数据挖掘实验室

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. 数据挖掘实验室

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.

数据挖掘研究院

Web search engines such as Google and Lycos are amongst the most visible applications of information retrieval research.

数据挖掘研究院

  数据挖掘研究院

Performance measures

There are various ways to measure how well the retrieved information matches the intended information: 数据挖掘论坛

数据挖掘工具

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. 数据挖掘交友

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. 数据挖掘研究院

Recall

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

数据挖掘交友

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:

数据挖掘交友

This is also known as the F₁ measure, because recall and precision are evenly weighted. 数据挖掘交友

The general formula is:

Two other commonly used F measures are the F_0.5 measure, which weights precision twice as much as recall, and the F₂ measure, which weights recall twice as much as precision.

数据挖掘工具

  数据挖掘研究院

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.

数据挖掘交友

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:

数据挖掘研究院

This method emphasizes returning more relevant documents earlier.

数据挖掘研究院

  数据挖掘交友

Model types

classification 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 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)

  数据挖掘研究院

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

  数据挖掘实验室

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.)

  数据挖掘实验室

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
• Information Storage and Retrieval Using Mumps(Online GPL Text)
• 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
• Sphinx Free open-source SQL full-text search engine
• 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

  数据挖掘交友

Major information retrieval research groups

• Center for Intelligent Information Retrieval
• CIR Centre for Information Retrieval
• Centre for Interactive Systems Research at City University, London
• IIT Information Retrieval Lab
• Information Retrieval at the Language Technologies Institute
• Information Retrieval at Microsoft Research Cambridge
• Information Retrieval Group at Université de Neuchâtel
• Glasgow Information Retrieval Group
• PSU Intelligent Systems Research Laboratory

  数据挖掘研究院

Major figures in information retrieval

• Calvin Mooers
• Eugene Garfield
• Gerard Salton
• W. Bruce Croft
• Karen Spärck Jones
• C. J. van Rijsbergen
• Stephen E. Robertson
• Abraham Bookstein

Awards in this field: Tony Kent Strix award.

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"