A definition of relevance for information retrieval

doi:10.1016/0020-0271(71)90024-6

Information Storage and Retrieval

Volume 7, Issue 1, June 1971, Pages 19-37

https://doi.org/10.1016/0020-0271(71)90024-6 Get rights and content

Abstract

The concept of “relevance”, sometimes also called “pertinence” or “aboutness”, is central to the theory of information retrieval. Unfortunately, however, there is at present no consensus as to how this notion should be defined. The purpose of this paper is to propose and defend a definition of what it means to say that a piece of stored information is “relevant” to the information need of a retrieval system user.

The suggested definition explicates relevance in terms of logical implication. For any yes-or-no question answering system which operates with one of the standard formalized languages, the definition provides a mathematically precise criterion of relevance. For other types of fact retrieval systems and reference retrieval systems, including all systems whose stored information is expressed in natural language, the definition is not mathematically precise but is nevertheless still helpful on a conceptual level.

References (19)

W. Goffman
Inform. Star. Retr.
(1964)
C.A. Cuadra et al.
Experimental Studies of Relevance Judgments
A.M. Rees et al.
A Field Experimental Approach to the Study of Relevance Assessments in Relation to Document Searching: Final Report
(1967)
C.A. Cuadra
On the Utility of the Relevance Concept
(1964)
C.A. Cuadra et al.
J. Docum.
(1967)
A.M. Rees et al.
The Measurability of Relevance
A.M. Rees
D.J. Hillman
Am. Docum.
(1964)
D.J. Hillman
On Concept-Formation and Relevance

There are more references available in the full text version of this article.

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Citation Excerpt :
Relevance has traditionally been studied from a logical or philosophical perspective (e.g., [5]). Also, for years, the notion of relevance has been addressed from an information retrieval perspective (e.g., [6]). However, to the best of the authors’ knowledge, relevance has never been analyzed from a topological perspective.
Relevance relations constitute the core of information retrieval. Topical ontologies, such as collaborative webpage classification projects, can provide a basis for identifying and analyzing such relations. New meaningful relevance relations can be automatically inferred from these ontologies by composing existing ones. In this work, several relevance propagation models are analyzed in terms of complex network theory. Structural properties such as Characteristic path length, Clustering coefficient and Degree distribution are computed over the models in order to understand the nature of each underlying network. This analysis raises interesting points about the Small-world and Scale-free structure of some relevance propagation models. Moreover, other connectivity and centrality measures are computed to gain additional insight into the topology of relevance. Finally, the analysis is complemented by providing visualizations of the k-core decomposition of different relevance propagation models. To illustrate the generalizability of the proposed methodology the analysis is carried out on an ontology from a different domain. The major theoretical implication of this analysis is the derivation of new instruments to typify semantic networks derived from relevance relations. The results can be exploited in a pragmatic way, as the parameters and properties derived by this analysis can serve as prior knowledge to algorithms for the automatic or semi-automatic construction of semantic networks.
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^☆: This work was carried out in part at the Institute of Library Research at the University of California under Office of Education grant OEG-1-7-071085-4256 (“An Information Processing Laboratory for Education and Research in Library Science”) and in part at the Graduate Library School of the University of Chicago under National Science Foundation grant GN-380 (“Studies in Indexing Depth and Retrieval Effectiveness”).

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A definition of relevance for information retrieval☆

Abstract

Inform. Star. Retr.

Experimental Studies of Relevance Judgments

A Field Experimental Approach to the Study of Relevance Assessments in Relation to Document Searching: Final Report

On the Utility of the Relevance Concept

J. Docum.

The Measurability of Relevance

Am. Docum.

On Concept-Formation and Relevance