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Relevance is a fundamental, though not completely understood, concept for documentation, information science, and information retrieval. This article presents the history of relevance through an exhaustive review of the literature. Such history being very complex (about 160 papers are discussed), it is not simple to describe it in a comprehensible way. Thus, first of all a framework for establishing a common ground is defined, and then the history itself is illustrated via the presentation in chronological order of the papers on relevance. The history is divided into three periods (“Before 1958,” “1959–1976,” and “1977–present”) and, inside each period, the papers on relevance are analyzed under seven different aspects (methodological foundations, different kinds of relevance, beyond-topical criteria adopted by users, modes for expression of the relevance judgment, dynamic nature of relevance, types of document representation, and agreement among different judges). © 1997 John Wiley & Sons, Inc.
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First, a new model of searching in online and other information systems, called ‘berrypicking’, is discussed. This model, it is argued, is much closer to the real behavior of information searchers than the traditional model of information retrieval is, and, consequently, will guide our thinking better in the design of effective interfaces. Second, the research literature of manual information seeking behavior is drawn on for suggestions of capabilities that users might like to have in online systems. Third, based on the new model and the research on information seeking, suggestions are made for how new search capabilities could be incorporated into the design of search interfaces. Particular attention is given to the nature and types of browsing that can be facilitated.
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The experimental evidence accumulated over the past 20 years indicates that text indexing systems based on the assignment of appropriately weighted single terms produce retrieval results that are superior to those obtainable with other more elaborate text representations. These results depend crucially on the choice of effective termweighting systems. This article summarizes the insights gained in automatic term weighting, and provides baseline single-term-indexing models with which other more elaborate content analysis procedures can be compared.
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The objectives of the study were to conduct a series of observations and experiments under as real-life situation as possible related to: (1) user context of questions in information retrieval; (2) the structure and classification of questions; (3) cognitive traits and decision making of searchers; and (4) diferent searches of the same question. The study is presented in three parts: Part I presents the background of the study and describes the models, measures, methods, procedures and statistical analyses used. Part II is devoted to results related to users, questions and effectiveness measures, and Part III to results related to searchers, searches and overlap studies. A concluding summary of all results is presented in Part III. © 1988 John Wiley & Sons, Inc.
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In a document retrieval, or other pattern matching environment where stored entities (documents) are compared with each other or with incoming patterns (search requests), it appears that the best indexing (property) space is one where each entity lies as far away from the others as possible; in these circumstances the value of an indexing system may be expressible as a function of the density of the object space; in particular, retrieval performance may correlate inversely with space density. An approach based on space density computations is used to choose an optimum indexing vocabulary for a collection of documents. Typical evaluation results are shown, demonstating the usefulness of the model.
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This paper reports on a novel technique for literature indexing and searching in a mechanized library system. The notion of relevance is taken as the key concept in the theory of information retrieval and a comparative concept of relevance is explicated in terms of the theory of probability. The resulting technique called “Probabilistic Indexing,” allows a computing machine, given a request for information, to make a statistical inference and derive a number (called the “relevance number”) for each document, which is a measure of the probability that the document will satisfy the given request. The result of a search is an ordered list of those documents which satisfy the request ranked according to their probable relevance. The paper goes on to show that whereas in a conventional library system the cross-referencing (“see” and “see also”) is based solely on the “semantical closeness” between index terms, statistical measures of closeness between index terms can be defined and computed. Thus, given an arbitrary request consisting of one (or many) index term(s), a machine can elaborate on it to increase the probability of selecting relevant documents that would not otherwise have been selected. Finally, the paper suggests an interpretation of the whole library problem as one where the request is considered as a clue on the basis of which the library system makes a concatenated statistical inference in order to provide as an output an ordered list of those documents which most probably satisfy the information needs of the user.
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