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Book Sections Year : 2007



Relevance is the extent to which some information is pertinent, connected, or applicable to the matter at hand. It represents a key concept in the fields of documentation, information science, and information retrieval. In information retrieval, the notion of relevance is used in three main contexts. Firstly, an algorithmic relevance score is assigned to a search result (usually a whole document) representing an estimated likelihood of relevance of the search result to a topic of request. This relevance score often determines the order in which search results are presented to the user. Secondly, when the performance of information retrieval systems is tested experimentally, the retrieved documents are assessed for their actual relevance to the topic of request by human assessors (topic experts). A binary relevance scale is typically used to assess the relevance of the search result, where the relevance is restricted to be either zero (when the result is not relevant to the user request) or one (when the result is relevant). Thirdly, in experiments involving users (or in operational settings) a broader notion of relevance is often used, with the aim of expressing the degree to which the retrieved documents are perceived as useful in solving the user's search task. In semi-structured text (XML) retrieval, the search result is typically an XML element, and the relevance score assigned by an XML retrieval system again represents an estimated likelihood of relevance of the search result to the topic of request. However, when the results are subsequently assessed for relevance, the binary relevance scale is not sufficient, primarily due to the hierarchical relationships that exist among the elements in an XML document. Accordingly, in XML retrieval one or more relevance dimensions (each with a multi-graded relevance scale) have been used to assess the relevance of the search result.
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inria-00174160 , version 1 (21-09-2007)


  • HAL Id : inria-00174160 , version 1


Jovan Pehcevski, Birger Larsen. Relevance. Ling Liu and M. Tamer Özsu. Encyclopedia of Database Systems, Springer-Verlag, 2007. ⟨inria-00174160⟩
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