Obtaining information resources relevant to an information need
Information retrieval (IR) in computing and information science is the process of obtaining information system resources that are relevant to an information need from a collection of those resources. Searches can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.
Automated information retrieval systems are used to reduce what has been called information overload. An IR system is a software system that provides access to books, journals and other documents; stores and manages those documents. Web search engines are the most visible IR applications.
Overview
An information retrieval process begins when a user enters a query into the system. Queries are formal statements of information needs, for example search strings in web search engines. In information retrieval a query does not uniquely identify a single object in the collection. Instead, several objects may match the query, perhaps with different degrees of relevancy.
An object is an entity that is represented by information in a content collection or database. User queries are matched against the database information. However, as opposed to classical SQL queries of a database, in information retrieval the results returned may or may not match the query, so results are typically ranked. This ranking of results is a key difference of information retrieval searching compared to database searching.
Depending on the application the data objects may be, for example, text documents, images, audio, mind maps or videos. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates or metadata.
Most IR systems compute a numeric score on how well each object in the database matches the query, and rank the objects according to this value. The top ranking objects are then shown to the user. The process may then be iterated if the user wishes to refine the query.
History
there is ... a machine called the Univac ... whereby letters and figures are coded as a pattern of magnetic spots on a long steel tape. By this means the text of a document, preceded by its subject code symbol, can be recorded ... the machine ... automatically selects and types out those references which have been coded in any desired way at a rate of 120 words a minute
— J. E. Holmstrom, 1948
The idea of using computers to search for relevant pieces of information was popularized in the article As We May Think by Vannevar Bush in 1945. It would appear that Bush was inspired by patents for a 'statistical machine' - filed by Emanuel Goldberg in the 1920s and '30s - that searched for documents stored on film. The first description of a computer searching for information was described by Holmstrom in 1948, detailing an early mention of the Univac computer. Automated information retrieval systems were introduced in the 1950s: one even featured in the 1957 romantic comedy, Desk Set. In the 1960s, the first large information retrieval research group was formed by Gerard Salton at Cornell. By the 1970s several different retrieval techniques had been shown to perform well on small text corpora such as the Cranfield collection (several thousand documents). Large-scale retrieval systems, such as the Lockheed Dialog system, came into use early in the 1970s.
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 evaluation of text retrieval methodologies on a very large text collection. This catalyzed research on methods that scale to huge corpora. The introduction of web search engines has boosted the need for very large scale retrieval systems even further.
Applications
Areas where information retrieval techniques are employed include (the entries are in alphabetical order within each category):
General applications
Digital libraries
Information filtering
Recommender systems
Media search
Blog search
Image retrieval
3D retrieval
Music retrieval
News search
Speech retrieval
Video retrieval
Search engines
Site search
Desktop search
Enterprise search
Federated search
Mobile search
Social search
Web search
Domain-specific applications
Expert search finding
Genomic information retrieval
Geographic information retrieval
Information retrieval for chemical structures
Information retrieval in software engineering
Legal information retrieval
Vertical search
Other retrieval methods
Methods/Techniques in which information retrieval techniques are employed include:
Adversarial information retrieval
Automatic summarization
Multi-document summarization
Compound term processing
Cross-lingual retrieval
Document classification
Spam filtering
Question answering
Model types
Categorization of IR-models (translated from German entry, original source Dominik Kuropka).
For effectively retrieving relevant documents by IR strategies, the documents are typically transformed into a suitable representation. Each retrieval strategy incorporates a specific model for its document representation purposes. The picture on the right illustrates the relationship of some common models. In the picture, the models are categorized according to two dimensions: the mathematical basis and the properties of the model.
First dimension: mathematical basis
Set-theoretic models represent documents as sets of words or phrases. 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. The similarity of the query vector and document vector is represented as a scalar value.
Probabilistic models treat the process of document retrieval as a probabilistic inference. Similarities are computed as probabilities that a document is relevant for a given query. Probabilistic theorems like the Bayes' theorem are often used in these models.
Binary Independence Model
Probabilistic relevance model on which is based the okapi (BM25) relevance function
Uncertain inference
Language models
Divergence-from-randomness model
Latent Dirichlet allocation
Feature-based retrieval models view documents as vectors of values of feature functions (or just features) and seek the best way to combine these features into a single relevance score, typically by learning to rank methods. Feature functions are arbitrary functions of document and query, and as such can easily incorporate almost any other retrieval model as just another feature.
Second dimension: properties of the model
Models without term-interdependencies treat different terms/words as independent. 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 variables.
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 rely an external source for the degree of interdependency between two terms. (For example, a human or sophisticated algorithms.)
Performance and correctness measures
Main article: Evaluation measures (information retrieval)
The evaluation of an information retrieval system' is the process of assessing how well a system meets the information needs of its users. In general, measurement considers a collection of documents to be searched and a search query. Traditional evaluation metrics, designed for Boolean retrieval[clarification needed] or top-k retrieval, include precision and recall. All measures assume a ground truth notion of relevancy: every document is known to be either relevant or non-relevant to a particular query. In practice, queries may be ill-posed and there may be different shades of relevancy.
Timeline
Before the 1900s
1801: Joseph Marie Jacquard invents the Jacquard loom, the first machine to use punched cards to control a sequence of operations.
1880s: Herman Hollerith invents an electro-mechanical data tabulator using punch cards as a machine readable medium.
1890 Hollerith cards, keypunches and tabulators used to process the 1890 US Census data.
1920s-1930s
Emanuel Goldberg submits patents for his "Statistical Machine” a document search engine that used photoelectric cells and pattern recognition to search the metadata on rolls of microfilmed documents.
1940s–1950s
late 1940s: The US military confronted problems of indexing and retrieval of wartime scientific research documents captured from Germans.
1945: Vannevar Bush's As We May Think appeared in Atlantic Monthly.
1947: Hans Peter Luhn (research engineer at IBM since 1941) began work on a mechanized punch card-based system for searching chemical compounds.
1950s: Growing concern in the US for a "science gap" with the USSR motivated, encouraged funding and provided a backdrop for mechanized literature searching systems (Allen Kent et al.) and the invention of the citation index by Eugene Garfield.
1950: The term "information retrieval" was coined by Calvin Mooers.
1951: Philip Bagley conducted the earliest experiment in computerized document retrieval in a master thesis at MIT.
1955: Allen Kent joined Case Western Reserve University, and eventually became associate director of the Center for Documentation and Communications Research. That same year, Kent and colleagues published a paper in American Documentation describing the precision and recall measures as well as detailing a proposed "framework" for evaluating an IR system which included statistical sampling methods for determining the number of relevant documents not retrieved.
1958: International Conference on Scientific Information Washington DC included consideration of IR systems as a solution to problems identified. See: Proceedings of the International Conference on Scientific Information, 1958 (National Academy of Sciences, Washington, DC, 1959)
1959: Hans Peter Luhn published "Auto-encoding of documents for information retrieval."
1960s:
early 1960s: Gerard Salton began work on IR at Harvard, later moved to Cornell.
1960: Melvin Earl Maron and John Lary Kuhns published "On relevance, probabilistic indexing, and information retrieval" in the Journal of the ACM 7(3):216–244, July 1960.
1962:
Cyril W. Cleverdon published early findings of the Cranfield studies, developing a model for IR system evaluation. See: Cyril W. Cleverdon, "Report on the Testing and Analysis of an Investigation into the Comparative Efficiency of Indexing Systems". Cranfield Collection of Aeronautics, Cranfield, England, 1962.
Kent published Information Analysis and Retrieval.
1963:
Weinberg report "Science, Government and Information" gave a full articulation of the idea of a "crisis of scientific information." The report was named after Dr. Alvin Weinberg.
Joseph Becker and Robert M. Hayes published text on information retrieval. Becker, Joseph; Hayes, Robert Mayo. Information storage and retrieval: tools, elements, theories. New York, Wiley (1963).
1964:
Karen Spärck Jones finished her thesis at Cambridge, Synonymy and Semantic Classification, and continued work on computational linguistics as it applies to IR.
The National Bureau of Standards sponsored a symposium titled "Statistical Association Methods for Mechanized Documentation." Several highly significant papers, including G. Salton's first published reference (we believe) to the SMART system.
mid-1960s:
National Library of Medicine developed MEDLARS Medical Literature Analysis and Retrieval System, the first major machine-readable database and batch-retrieval system.
Project Intrex at MIT.
1965: J. C. R. Licklider published Libraries of the Future.
1966: Don Swanson was involved in studies at University of Chicago on Requirements for Future Catalogs.
late 1960s: F. Wilfrid Lancaster completed evaluation studies of the MEDLARS system and published the first edition of his text on information retrieval.
1968:
Gerard Salton published Automatic Information Organization and Retrieval.
John W. Sammon, Jr.'s RADC Tech report "Some Mathematics of Information Storage and Retrieval..." outlined the vector model.
1969: Sammon's "A nonlinear mapping for data structure analysis" (IEEE Transactions on Computers) was the first proposal for visualization interface to an IR system.
1970s
early 1970s:
First online systems—NLM's AIM-TWX, MEDLINE; Lockheed's Dialog; SDC's ORBIT.
Theodor Nelson promoting concept of hypertext, published Computer Lib/Dream Machines.
1971: Nicholas Jardine and Cornelis J. van Rijsbergen published "The use of hierarchic clustering in information retrieval", which articulated the "cluster hypothesis."
1975: Three highly influential publications by Salton fully articulated his vector processing framework and term discrimination model:
A Theory of Indexing (Society for Industrial and Applied Mathematics)
A Theory of Term Importance in Automatic Text Analysis (JASIS v. 26)
A Vector Space Model for Automatic Indexing (CACM 18:11)
1978: The First ACM SIGIR conference.
1979: C. J. van Rijsbergen published Information Retrieval (Butterworths). Heavy emphasis on probabilistic models.
1979: Tamas Doszkocs implemented the CITE natural language user interface for MEDLINE at the National Library of Medicine. The CITE system supported free form query input, ranked output and relevance feedback.
1980s
1980: First international ACM SIGIR conference, joint with British Computer Society IR group in Cambridge.
1982: Nicholas J. Belkin, Robert N. Oddy, and Helen M. Brooks proposed the ASK (Anomalous State of Knowledge) viewpoint for information retrieval. This was an important concept, though their automated analysis tool proved ultimately disappointing.
1983: Salton (and Michael J. McGill) published Introduction to Modern Information Retrieval (McGraw-Hill), with heavy emphasis on vector models.
1985: David Blair and Bill Maron publish: An Evaluation of Retrieval Effectiveness for a Full-Text Document-Retrieval System
mid-1980s: Efforts to develop end-user versions of commercial IR systems.
1985–1993: Key papers on and experimental systems for visualization interfaces.
Work by Donald B. Crouch, Robert R. Korfhage, Matthew Chalmers, Anselm Spoerri and others.
1989: First World Wide Web proposals by Tim Berners-Lee at CERN.
1990s
1992: First TREC conference.
1997: Publication of Korfhage's Information Storage and Retrieval with emphasis on visualization and multi-reference point systems.
1999: Publication of Ricardo Baeza-Yates and Berthier Ribeiro-Neto's Modern Information Retrieval by Addison Wesley, the first book that attempts to cover all IR.
late 1990s: Web search engines implementation of many features formerly found only in experimental IR systems. Search engines become the most common and maybe best instantiation of IR models.
Major conferences
SIGIR: Conference on Research and Development in Information Retrieval
ECIR: European Conference on Information Retrieval
CIKM: Conference on Information and Knowledge Management
WWW: International World Wide Web Conference
WSDM: Conference on Web Search and Data Mining
ICTIR: International Conference on Theory of Information Retrieval
Awards in the field
Tony Kent Strix award
Gerard Salton Award
Karen Spärck Jones Award
See also
Adversarial information retrieval – Information retrieval strategies in datasets
Computer memory – Device used on a computer for storing data
Controlled vocabulary – Method of organizing knowledge
Cross-language information retrieval
Data mining – Process of extracting and discovering patterns in large data sets
European Summer School in Information Retrieval
Human–computer information retrieval (HCIR)
Information extraction – Automatically extracting structured information from un- or semi-structured machine-readable documents, such as human language texts
Information seeking – Process or activity of attempting to obtain information in both human and technological contexts
Information seeking § Compared to information retrieval
Collaborative information seeking
Social information seeking
Information Retrieval Facility
Knowledge visualization
Multimedia information retrieval
Personal information management – Tools and systems for managing one's own data
Query understanding
Relevance (information retrieval)
Relevance feedback
Rocchio classification
Search engine indexing – Call sweetheart
Special Interest Group on Information Retrieval
Subject indexing
Temporal information retrieval
tf–idf – Number that reflects the importance of a word to a document in a corpus
XML retrieval
Web mining
References
Further reading
External links
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Gerard Salton Award
The Gerard Salton Award is presented by the Association for Computing Machinery Special Interest Group on Information Retrieval every three years to an individual who has made "significant, sustained and continuing
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