-
Buckland, M.K.; Liu, Z.: History of information science (1995)
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- Abstract
- State of the art review of the historical development of information science as deemed to be covered by the particular interests of memebers of the American Society for Information Science, as defined as the representation, storage, transmission, selection, retrieval, filtering, and use of documents and messages. Arranges the references cited roughly according to the classification scheme used by Information Science Abstracts, and so uses the headings: background; information science; techniques and technology; information related behaviour; application areas; social aspects; education for information science; institutions; individuals; geographical areas; and conclusions
- Date
- 13. 6.1996 19:22:20
- Source
- Annual review of information science and technology. 30(1995), S.385-416
-
Mostafa, J.: Digital image representation and access (1994)
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- Abstract
- State of the art review of techniques used to generate, store and retrieval digital images. Explains basic terms and concepts related to image representation and describes the differences between bilevel, greyscale, and colour images. Introduces additional image related data, specifically colour standards, correction values, resolution parameters and lookup tables. Illustrates the use of data compression techniques and various image data formats that have been used. Identifies 4 branches of imaging research related to dtaa indexing and modelling: verbal indexing; visual surrogates; image indexing; and data structures. Concludes with a discussion of the state of the art in networking technology with consideration of image distribution, local system requirements and data integrity
- Source
- Annual review of information science and technology. 29(1994), S.91-135
-
Enser, P.G.B.: Visual image retrieval (2008)
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- Date
- 22. 1.2012 13:01:26
- Source
- Annual review of information science and technology. 42(2008), S.3-42
-
Belkin, N.J.; Croft, W.B.: Retrieval techniques (1987)
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- Source
- Annual review of information science and technology. 22(1987), S.109-145
-
Smith, L.C.: Artificial intelligence and information retrieval (1987)
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- Source
- Annual review of information science and technology. 22(1987), S.41-77
-
Drenth, H.; Morris, A.; Tseng, G.: Expert systems as information intermediaries (1991)
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- Abstract
- Points out that expert systems have great potential to enhance access to information retrieval systems as they use expertise to carry out tasks such as diagnosis and planning and make expertise available to nonexperts. Potential end users of online information retrieval systems are frequently deterred by the complexity of theses systems. Expert systems can mediate between the searcher and the information retrieval system and might be the key both to increasing and end user searching and to improving the quality of searches overall
- Source
- Annual review of information science and technology. 26(1991), S.113-154
-
Blair, D.C.: Information retrieval and the philosophy of language (2002)
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- Abstract
- Information retrieval - the retrieval, primarily, of documents or textual material - is fundamentally a linguistic process. At the very least we must describe what we want and match that description with descriptions of the information that is available to us. Furthermore, when we describe what we want, we must mean something by that description. This is a deceptively simple act, but such linguistic events have been the grist for philosophical analysis since Aristotle. Although there are complexities involved in referring to authors, document types, or other categories of information retrieval context, here I wish to focus an one of the most problematic activities in information retrieval: the description of the intellectual content of information items. And even though I take information retrieval to involve the description and retrieval of written text, what I say here is applicable to any information item whose intellectual content can be described for retrieval-books, documents, images, audio clips, video clips, scientific specimens, engineering schematics, and so forth. For convenience, though, I will refer only to the description and retrieval of documents. The description of intellectual content can go wrong in many obvious ways. We may describe what we want incorrectly; we may describe it correctly but in such general terms that its description is useless for retrieval; or we may describe what we want correctly, but misinterpret the descriptions of available information, and thereby match our description of what we want incorrectly. From a linguistic point of view, we can be misunderstood in the process of retrieval in many ways. Because the philosophy of language deals specifically with how we are understood and mis-understood, it should have some use for understanding the process of description in information retrieval. First, however, let us examine more closely the kinds of misunderstandings that can occur in information retrieval. We use language in searching for information in two principal ways. We use it to describe what we want and to discriminate what we want from other information that is available to us but that we do not want. Description and discrimination together articulate the goals of the information search process; they also delineate the two principal ways in which language can fail us in this process. Van Rijsbergen (1979) was the first to make this distinction, calling them "representation" and "discrimination.""
- Source
- Annual review of information science and technology. 37(2003), S.3-50
-
Rasmussen, E.M.: Indexing images (1997)
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- Abstract
- State of the art review of methods available for accessing collections of digital images by means of manual and automatic indexing. Distinguishes between concept based indexing, in which images and the objects represented, are manually identified and described in terms of what they are and represent, and content based indexing, in which features of images (such as colours) are automatically identified and extracted. The main discussion is arranged in 6 sections: studies of image systems and their use; approaches to indexing images; image attributes; concept based indexing; content based indexing; and browsing in image retrieval. The performance of current image retrieval systems is largely untested and they still lack an extensive history and tradition of evaluation and standards for assessing performance. Concludes that there is a significant amount of research to be done before image retrieval systems can reach the state of development of text retrieval systems
- Source
- Annual review of information science and technology. 32(1997), S.169-196
-
Rothenberg, D.: Information technology in education (1994)
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- Abstract
- State of the art review of the application of information technology in teacher education, teaching and education in the period 1991 to 1993. Notes that as schools move away from the information accumulation model of education to the learning by doing model, the use of information technology in education will become increasingly considered an essential part of school reform. Virtually all US schools use microcomputers, most use CD-ROM, about half have local area networks development
- Source
- Annual review of information science and technology. 29(1994), S.277-302
-
Kantor, P.B.: Information retrieval techniques (1994)
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- Abstract
- State of the art review of information retrieval techniques viewed in terms of the growing effort to implement concept based retrieval in content based algorithms. Identifies trends in the automation of indexing, retrieval, and the interaction between systems and users. Identifies 3 central issues: ways in which systems describe documents for purposes of information retrieval; ways in which systems compute the degree of match between a given document and the current state of the query; amd what the systems do with the information that they obtain from the users. Looks at information retrieval techniques in terms of: location, navigation; indexing; documents; queries; structures; concepts; matching documents to queries; restoring query structure; algorithms and content versus concepts; formulation of concepts in terms of contents; formulation of concepts with the assistance of the users; complex system codes versus underlying principles; and system evaluation
- Source
- Annual review of information science and technology. 29(1994), S.53-90
-
Lima, G.A. de; Castro, I.R.: Uso da classificacao decimal universal para a recuperacao da informacao em ambientes digitas : uma revisao sistematica da literatura (2021)
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- Abstract
- Knowledge Organization Systems, even traditional ones, such as the Universal Decimal Classification, have been studied to improve the retrieval of information online, although the potential of using knowledge structures in the user interface has not yet been widespread. Objective: This study presents a mapping of scientific production on information retrieval methodologies, which make use of the Universal Decimal Classification. Methodology: Systematic Literature Review, conducted in two stages, with a selection of 44 publications, resulting in the time interval from 1964 to 2017, whose categories analyzed were: most productive authors, languages of publications, types of document, year of publication, most cited work, major impact journal, and thematic categories covered in the publications. Results: A total of nine more productive authors and co-authors were found; predominance of the English language (42 publications); works published in the format of journal articles (33); and highlight to the year 2007 (eight publications). In addition, it was identified that the most cited work was by Mcilwaine (1997), with 61 citations, and the journal Extensions & Corrections to the UDC was the one with the largest number of publications, in addition to the incidence of the theme Universal Automation linked to a thesaurus for information retrieval, present in 19 works. Conclusions: Shortage of studies that explore the potential of the Decimal Classification, especially in Brazilian literature, which highlights the need for further study on the topic, involving research at the national and international levels.
- Footnote
- Englischer Titel: Use of the Universal Decimal Classification for the recoery of information in digital environments: a systematic review of literature.
- Theme
- Klassifikationssysteme im Online-Retrieval
-
Dumais, S.T.: Latent semantic analysis (2003)
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- Abstract
- Latent Semantic Analysis (LSA) was first introduced in Dumais, Furnas, Landauer, and Deerwester (1988) and Deerwester, Dumais, Furnas, Landauer, and Harshman (1990) as a technique for improving information retrieval. The key insight in LSA was to reduce the dimensionality of the information retrieval problem. Most approaches to retrieving information depend an a lexical match between words in the user's query and those in documents. Indeed, this lexical matching is the way that the popular Web and enterprise search engines work. Such systems are, however, far from ideal. We are all aware of the tremendous amount of irrelevant information that is retrieved when searching. We also fail to find much of the existing relevant material. LSA was designed to address these retrieval problems, using dimension reduction techniques. Fundamental characteristics of human word usage underlie these retrieval failures. People use a wide variety of words to describe the same object or concept (synonymy). Furnas, Landauer, Gomez, and Dumais (1987) showed that people generate the same keyword to describe well-known objects only 20 percent of the time. Poor agreement was also observed in studies of inter-indexer consistency (e.g., Chan, 1989; Tarr & Borko, 1974) in the generation of search terms (e.g., Fidel, 1985; Bates, 1986), and in the generation of hypertext links (Furner, Ellis, & Willett, 1999). Because searchers and authors often use different words, relevant materials are missed. Someone looking for documents an "human-computer interaction" will not find articles that use only the phrase "man-machine studies" or "human factors." People also use the same word to refer to different things (polysemy). Words like "saturn," "jaguar," or "chip" have several different meanings. A short query like "saturn" will thus return many irrelevant documents. The query "Saturn Gar" will return fewer irrelevant items, but it will miss some documents that use only the terms "Saturn automobile." In searching, there is a constant tension between being overly specific and missing relevant information, and being more general and returning irrelevant information.
A number of approaches have been developed in information retrieval to address the problems caused by the variability in word usage. Stemming is a popular technique used to normalize some kinds of surface-level variability by converting words to their morphological root. For example, the words "retrieve," "retrieval," "retrieved," and "retrieving" would all be converted to their root form, "retrieve." The root form is used for both document and query processing. Stemming sometimes helps retrieval, although not much (Harman, 1991; Hull, 1996). And, it does not address Gases where related words are not morphologically related (e.g., physician and doctor). Controlled vocabularies have also been used to limit variability by requiring that query and index terms belong to a pre-defined set of terms. Documents are indexed by a specified or authorized list of subject headings or index terms, called the controlled vocabulary. Library of Congress Subject Headings, Medical Subject Headings, Association for Computing Machinery (ACM) keywords, and Yellow Pages headings are examples of controlled vocabularies. If searchers can find the right controlled vocabulary terms, they do not have to think of all the morphologically related or synonymous terms that authors might have used. However, assigning controlled vocabulary terms in a consistent and thorough manner is a time-consuming and usually manual process. A good deal of research has been published about the effectiveness of controlled vocabulary indexing compared to full text indexing (e.g., Bates, 1998; Lancaster, 1986; Svenonius, 1986). The combination of both full text and controlled vocabularies is often better than either alone, although the size of the advantage is variable (Lancaster, 1986; Markey, Atherton, & Newton, 1982; Srinivasan, 1996). Richer thesauri have also been used to provide synonyms, generalizations, and specializations of users' search terms (see Srinivasan, 1992, for a review). Controlled vocabularies and thesaurus entries can be generated either manually or by the automatic analysis of large collections of texts.
With the advent of large-scale collections of full text, statistical approaches are being used more and more to analyze the relationships among terms and documents. LSA takes this approach. LSA induces knowledge about the meanings of documents and words by analyzing large collections of texts. The approach simultaneously models the relationships among documents based an their constituent words, and the relationships between words based an their occurrence in documents. By using fewer dimensions for representation than there are unique words, LSA induces similarities among terms that are useful in solving the information retrieval problems described earlier. LSA is a fully automatic statistical approach to extracting relations among words by means of their contexts of use in documents, passages, or sentences. It makes no use of natural language processing techniques for analyzing morphological, syntactic, or semantic relations. Nor does it use humanly constructed resources like dictionaries, thesauri, lexical reference systems (e.g., WordNet), semantic networks, or other knowledge representations. Its only input is large amounts of texts. LSA is an unsupervised learning technique. It starts with a large collection of texts, builds a term-document matrix, and tries to uncover some similarity structures that are useful for information retrieval and related text-analysis problems. Several recent ARIST chapters have focused an text mining and discovery (Benoit, 2002; Solomon, 2002; Trybula, 2000). These chapters provide complementary coverage of the field of text analysis.
- Source
- Annual review of information science and technology. 38(2004), S.189-230
-
Schamber, L.: Relevance and information behavior (1994)
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- Abstract
- State of the art review of relevance as it relates to the behaviour of users seeking and using information rather than in evaluating the performance of information retrieval systems. Views relevance as a manifestation of human information behaviour and excludes works that view relevance only as matching or computational functions of information retrieval systems
- Source
- Annual review of information science and technology. 29(1994), S.3-48
-
Julien, C.-A.; Leide, J.E.; Bouthillier, F.: Controlled user evaluations of information visualization interfaces for text retrieval : literature review and meta-analysis (2008)
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- Abstract
- This review describes experimental designs (users, search tasks, measures, etc.) used by 31 controlled user studies of information visualization (IV) tools for textual information retrieval (IR) and a meta-analysis of the reported statistical effects. Comparable experimental designs allow research designers to compare their results with other reports, and support the development of experimentally verified design guidelines concerning which IV techniques are better suited to which types of IR tasks. The studies generally use a within-subject design with 15 or more undergraduate students performing browsing to known-item tasks on sets of at least 1,000 full-text articles or Web pages on topics of general interest/news. Results of the meta-analysis (N = 8) showed no significant effects of the IV tool as compared with a text-only equivalent, but the set shows great variability suggesting an inadequate basis of comparison. Experimental design recommendations are provided which would support comparison of existing IV tools for IR usability testing.
- Source
- Journal of the American Society for Information Science and Technology. 59(2008) no.6, S.1012-1024
-
Liu, X.; Croft, W.B.: Statistical language modeling for information retrieval (2004)
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- Abstract
- This chapter reviews research and applications in statistical language modeling for information retrieval (IR), which has emerged within the past several years as a new probabilistic framework for describing information retrieval processes. Generally speaking, statistical language modeling, or more simply language modeling (LM), involves estimating a probability distribution that captures statistical regularities of natural language use. Applied to information retrieval, language modeling refers to the problem of estimating the likelihood that a query and a document could have been generated by the same language model, given the language model of the document either with or without a language model of the query. The roots of statistical language modeling date to the beginning of the twentieth century when Markov tried to model letter sequences in works of Russian literature (Manning & Schütze, 1999). Zipf (1929, 1932, 1949, 1965) studied the statistical properties of text and discovered that the frequency of works decays as a Power function of each works rank. However, it was Shannon's (1951) work that inspired later research in this area. In 1951, eager to explore the applications of his newly founded information theory to human language, Shannon used a prediction game involving n-grams to investigate the information content of English text. He evaluated n-gram models' performance by comparing their crossentropy an texts with the true entropy estimated using predictions made by human subjects. For many years, statistical language models have been used primarily for automatic speech recognition. Since 1980, when the first significant language model was proposed (Rosenfeld, 2000), statistical language modeling has become a fundamental component of speech recognition, machine translation, and spelling correction.
- Source
- Annual review of information science and technology. 39(2005), S.3-32
-
Bath, P.A.: Data mining in health and medical information (2003)
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- Abstract
- Data mining (DM) is part of a process by which information can be extracted from data or databases and used to inform decision making in a variety of contexts (Benoit, 2002; Michalski, Bratka & Kubat, 1997). DM includes a range of tools and methods for extractiog information; their use in the commercial sector for knowledge extraction and discovery has been one of the main driving forces in their development (Adriaans & Zantinge, 1996; Benoit, 2002). DM has been developed and applied in numerous areas. This review describes its use in analyzing health and medical information.
- Date
- 23.10.2005 18:29:03
- Source
- Annual review of information science and technology. 38(2004), S.331-370
-
Hjoerland, B.; Kyllesbech Nielsen, L.: Subject access points in electronic retrieval (2001)
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- Source
- Annual review of information science and technology. 35(2001), S.249-298
- Theme
- Klassifikationssysteme im Online-Retrieval
Verbale Doksprachen im Online-Retrieval
-
Case, D.O.: Looking for information : a survey on research on information seeking, needs, and behavior (2002)
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- Footnote
- Rez. in: JASIST 54(2003) no.7, S.695-697 (R. Savolainen): "Donald O. Case has written an ambitious book to create an overall picture of the major approaches to information needs and seeking (INS) studies. The aim to write an extensive review is reflected in the list of references containing about 700 items. The high ambitions are explained an p. 14, where Case states that he is aiming at a multidisciplinary understanding of the concept of information seeking. In the Preface, the author characterizes his book as an introduction to the topic for students at the graduate level, as well as as a review and handbook for scholars engagged in information behavior research. In my view, Looking for Information is particularly welcome as an academic textbook because the field of INS studies suffers from the lack of monographs. Along with the continuous growth of the number of journal articles and conference papers, there is a genuine need for a book that picks up the numerous pieces and puts them together. The use of the study as a textbook is facilitated by clearly delineated sections an major themes and the wealth of concrete examples of information seeking in everyday contexts. The book is lucidly written and it is accessible to novice readers, too. At first glance, the idea of providing a comprehensive review of INS studies may seem a mission impossible because the current number of articles, papers, and other contributions in this field is nearing the 10,000 range (p. 224). Donald Case is not alone in the task of coming to grips with an increasing number of studies; similar problems have been faced by those writing INS-related chapters for the Annual Review of Information Science and Technology (ARIST). Case has solved the problem of "too many publications to be reviewed" by concentrating an the INS literature published during the last two decades. Secondly, studies an library use and information retrieval are discussed only to a limited extent. In addition, Case is highly selective as to studies focusing an the use of specific sources and channels such as WWW. These delineations are reasonable, even though they beg some questions. First, how should one draw the line between studies an information seeking and information retrieval? Case does not discuss this question in greater detail, although in recent years, the overlapping areas of information seeking and retrieval studies have been broadened, along with the growing importance of WWW in information seeking/retrieval. Secondly, how can one define the concept of information searching (or, more specifically, Internet or Web searching) in relation to information seeking and information retrieval? In the field of Web searching studies, there is an increasing number of contributions that are of direct relevance to information-seeking studies. Clearly, the advent of the Internet, particularly, the Web, has blurred the previous lines between INS and IR literature, making them less clear cut. The book consists of five main sections, and comprises 13 chapters. There is an Appendix serving the needs of an INS textbook (questions for discussion and application). The structure of the book is meticulously planned and, as a whole, it offers a sufficiently balanced contribution to theoretical, methodological, and empirical issues of INS. The title, Looking for Information: A Survey of Research an Information Seeking, Needs, and Behavior aptly describes the main substance of the book. . . . It is easy to agree with Case about the significance of the problem of specialization and fragmentation. This problem seems to be concomitant with the broadening field of INS research. In itself, Case's book can be interpreted as a struggle against this fragmentation. His book suggests that this struggle is not hopeless and that it is still possible to draw an overall picture of the evolving research field. The major pieces of the puzzle were found and the book will provide a useful overview of INS studies for many years."
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Rasmussen, E.M.: Indexing and retrieval for the Web (2002)
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- Abstract
- The introduction and growth of the World Wide Web (WWW, or Web) have resulted in a profound change in the way individuals and organizations access information. In terms of volume, nature, and accessibility, the characteristics of electronic information are significantly different from those of even five or six years ago. Control of, and access to, this flood of information rely heavily an automated techniques for indexing and retrieval. According to Gudivada, Raghavan, Grosky, and Kasanagottu (1997, p. 58), "The ability to search and retrieve information from the Web efficiently and effectively is an enabling technology for realizing its full potential." Almost 93 percent of those surveyed consider the Web an "indispensable" Internet technology, second only to e-mail (Graphie, Visualization & Usability Center, 1998). Although there are other ways of locating information an the Web (browsing or following directory structures), 85 percent of users identify Web pages by means of a search engine (Graphie, Visualization & Usability Center, 1998). A more recent study conducted by the Stanford Institute for the Quantitative Study of Society confirms the finding that searching for information is second only to e-mail as an Internet activity (Nie & Ebring, 2000, online). In fact, Nie and Ebring conclude, "... the Internet today is a giant public library with a decidedly commercial tilt. The most widespread use of the Internet today is as an information search utility for products, travel, hobbies, and general information. Virtually all users interviewed responded that they engaged in one or more of these information gathering activities."
Techniques for automated indexing and information retrieval (IR) have been developed, tested, and refined over the past 40 years, and are well documented (see, for example, Agosti & Smeaton, 1996; BaezaYates & Ribeiro-Neto, 1999a; Frakes & Baeza-Yates, 1992; Korfhage, 1997; Salton, 1989; Witten, Moffat, & Bell, 1999). With the introduction of the Web, and the capability to index and retrieve via search engines, these techniques have been extended to a new environment. They have been adopted, altered, and in some Gases extended to include new methods. "In short, search engines are indispensable for searching the Web, they employ a variety of relatively advanced IR techniques, and there are some peculiar aspects of search engines that make searching the Web different than more conventional information retrieval" (Gordon & Pathak, 1999, p. 145). The environment for information retrieval an the World Wide Web differs from that of "conventional" information retrieval in a number of fundamental ways. The collection is very large and changes continuously, with pages being added, deleted, and altered. Wide variability between the size, structure, focus, quality, and usefulness of documents makes Web documents much more heterogeneous than a typical electronic document collection. The wide variety of document types includes images, video, audio, and scripts, as well as many different document languages. Duplication of documents and sites is common. Documents are interconnected through networks of hyperlinks. Because of the size and dynamic nature of the Web, preprocessing all documents requires considerable resources and is often not feasible, certainly not an the frequent basis required to ensure currency. Query length is usually much shorter than in other environments-only a few words-and user behavior differs from that in other environments. These differences make the Web a novel environment for information retrieval (Baeza-Yates & Ribeiro-Neto, 1999b; Bharat & Henzinger, 1998; Huang, 2000).
- Source
- Annual review of information science and technology. 37(2003), S.91-126
-
Rader, H.B.: Library orientation and instruction - 1993 (1994)
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- Abstract
- This annotated bibliography lists materials dealing with information literacy - including instruction in the use of information resources, research, and computer skills related to retrieving, using, and evaluating information. This review, the 20th to be published in RSR, includes items in English published in 1993
- Source
- Reference services review. 22(1994) no.4, S.81-
