Information retrieval
Introduction
[edit | edit source]Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other content-based indexing. Information retrieval is the science[1] 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; it also stores and manages those documents. Web search engines are the most visible IR applications.
Learning Tasks
[edit | edit source]- (Information Systems) Explore the learning resource about Information Systems and identify the links between different information systems and information retrieval.
- (Mathematical Foundations) Explain, how mathematics can be used to handle
- search requests,
- how to represent knowledge
- (Web Crawler) What is web crawler and why is it necessary to rely on web crawlers to create e.g. an web index to handle search requests of users.
Learning Modules
[edit | edit source]Overview
[edit | edit source]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 relevance.
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.[2]
Depending on the application the data objects may be, for example, text documents, images,[3] audio,[4] mind maps[5] 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.[6]
Applications
[edit | edit source]Areas where information retrieval techniques are employed include (the entries are in alphabetical order within each category):
General applications
[edit | edit source]- Digital libraries
- Information filtering
- Media search
- Blog search
- Image retrieval
- 3D retrieval
- Music retrieval
- News search
- Speech retrieval
- Video retrieval
- Search engines
Domain-specific applications
[edit | edit source]- 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
[edit | edit source]Methods/Techniques in which information retrieval techniques are employed include:
- Adversarial information retrieval
- Automatic summarization
- Compound term processing
- Cross-lingual retrieval
- Document classification
- Spam filtering
- Question answering
Major conferences
[edit | edit source]- 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
[edit | edit source]See also
[edit | edit source]- Adversarial information retrieval
- Computer memory
- Controlled vocabulary
- Cross-language information retrieval
- Data mining
- Data retrieval
- European Summer School in Information Retrieval
- abbreviation=HCIR
- Information extraction
- Information seeking
- Information Retrieval Facility
- Knowledge visualization
- Multimedia information retrieval
- Personal information management
- Pearl growing
- Query understanding
- Relevance (information retrieval)
- Relevance feedback
- Rocchio classification
- Search engine indexing
- Special Interest Group on Information Retrieval
- Subject indexing
- Temporal information retrieval
- tf–idf
- XML retrieval
- Web mining
References
[edit | edit source]- ↑ Luk, R. W. P. (2022). "Why is information retrieval a scientific discipline?". Foundations of Science 27 (2): 427–453. doi:10.1007/s10699-020-09685-x.
- ↑ Jansen, B. J. and Rieh, S. (2010) The Seventeen Theoretical Constructs of Information Searching and Information Retrieval Archived 2016-03-04 at the Wayback Machine. Journal of the American Society for Information Sciences and Technology. 61(8), 1517-1534.
- ↑ Goodrum, Abby A. (2000). "Image Information Retrieval: An Overview of Current Research". Informing Science 3 (2).
- ↑ Foote, Jonathan (1999). "An overview of audio information retrieval". Multimedia Systems 7: 2–10. doi:10.1007/s005300050106.
- ↑ Beel, Jöran; Gipp, Bela; Stiller, Jan-Olaf (2009). Information Retrieval On Mind Maps - What Could It Be Good For?. Proceedings of the 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom'09). Washington, DC: IEEE. Archived from the original on 2011-05-13. Retrieved 2012-03-13.
- ↑ Frakes, William B.; Baeza-Yates, Ricardo (1992). Information Retrieval Data Structures & Algorithms. Prentice-Hall, Inc.. ISBN 978-0-13-463837-9. https://www.scribd.com/doc/13742235/Information-Retrieval-Data-Structures-Algorithms-William-B-Frakes.
Further reading
[edit | edit source]- Ricardo Baeza-Yates, Berthier Ribeiro-Neto. Modern Information Retrieval: The Concepts and Technology behind Search (second edition) Archived 2017-09-18 at the Wayback Machine. Addison-Wesley, UK, 2011.
- Stefan Büttcher, Charles L. A. Clarke, and Gordon V. Cormack. Information Retrieval: Implementing and Evaluating Search Engines Archived 2020-10-05 at the Wayback Machine. MIT Press, Cambridge, Massachusetts, 2010.
- "Information Retrieval System". Library & Information Science Network. 24 April 2015. Archived from the original on 11 May 2020. Retrieved 3 May 2020.
- Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. Introduction to Information Retrieval. Cambridge University Press, 2008.
- Yeo, ShinJoung. (2023) Behind the Search Box: Google and the Global Internet Industry (U of Illinois Press, 2023) ISBN 10:0252087127 online
External links
[edit | edit source]Search Wikiquote for quotations related to: Information retrieval |
- ACM SIGIR: Information Retrieval Special Interest Group
- BCS IRSG: British Computer Society – Information Retrieval Specialist Group
- Text Retrieval Conference (TREC)
- Forum for Information Retrieval Evaluation (FIRE)
- Information Retrieval (online book) by C. J. van Rijsbergen
- Information Retrieval Wiki Archived 2015-11-24 at the Wayback Machine
- Information Retrieval Facility Archived 2008-05-22 at the Wayback Machine
- TREC report on information retrieval evaluation techniques
- How eBay measures search relevance
- Information retrieval performance evaluation tool @ Athena Research Centre
Page Information
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