Relevance (information science)

In information science , the question of the relevance of documents is an important point in information retrieval and an important criterion of information quality. A distinction must be made between subjective relevance (pertinence), objective relevance, estimated relevance and situational relevance.

Relevance types

• Subjective relevance (pertinence) is the "relationship between a document and a person's need for information" and indicates the usefulness of the document as perceived by the user in relation to the person's need for information.
• Objective relevance is a construct that indicates the relationship between a request for information and a proposed document assessed by neutral observers (group of experts).
• Estimated relevance (system relevance) is also a "relationship between a query and a document", which results from rules with which an EDP search system weights the importance of a document for a search query (= relevance value).
• Situational relevance is the (actual) usefulness of the document in relation to the task from which the need for information arose.

Objective relevance of a document

A document is (objectively) relevant to a search query,

• if it serves objectively to prepare a decision,
• if it objectively closes a knowledge gap,
• if it objectively fulfills an early warning function.

Search engines use criteria of relevance to sort documents when they are output (“relevance ranking”). There are different types of distribution of the relevance of documents on a topic, of which the informetric (power law) and the inverse-logistic are the most important.

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• Stefano Mizzaro: Relevance: The whole history. In: Journal of the American Society for Information Science. 48, 1997, pp. 810-832.
• Wolfgang G. Stock: On relevance distributions (PDF). In: Journal of the American Society for Information Science and Technology. 57, 2006, pp. 1126-1129.
• Wolfgang G. Stock: Information Retrieval. Search and find information. Oldenbourg, Munich 2007, ISBN 978-3-486-58172-0 , pp. 68-81 (Chapter 6, Relevance and Pertinence).

Web links

• Norbert Fuhr: Introduction to Information Retrieval, script for the lecture in SS 10 (PDF; 1.0 MB), April 9, 2010.
• Michael Joachim Nebel: Secure information acquisition - A lecture as part of the STEDAC conference on April 21, 2006 (PDF; 80 kB).

Individual evidence

1. ↑ Mario Rese, Gernot Gräfe, Valerie Herter: Lack of relevance as an information quality problem in corporate decision-making processes (PDF; 337 kB).
2. ↑ swisseduc.ch
3. ↑ swisseduc.ch
4. ↑ Jens E. Wolff: Lecture Information Retrieval Winter Semester 04/05 ( Memento of the original from July 27, 2012 in the Internet Archive ) Info: The archive link was automatically inserted and not yet checked. Please check the original and archive link according to the instructions and then remove this notice. (PDF; 171 kB); Institute for Computer Science III, University of Bonn, November 25, 2004.