Search (20 results, page 1 of 1)

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Date

13. 7.2008 19:53:22

Theme

Citation indexing

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Abstract

In the early 1990s, much of the enthusiasm for the use of electronic media to enhance scholarly communication focused an electronic journals, especially electronic-only, (pure) e journals (see for example, Peek & Newby's [1996] anthology). Much of the systematic research an the use of electronic media to enhance scholarly communication also focused an electronic journals. However, by the late 1990s, numerous scientific publishers had transformed their paper journals (p journals) into paper and electronic journals (p-e journals) and sold them via subscription models that did not provide the significant costs savings, speed of access, or breadth of audience that pure e -journal advocates had expected (Okerson, 1996). In 2001, a group of senior life scientists led a campaign to have publishers make their journals freely available online six months after publication (Russo, 2001). The campaign leaders, using the name "Public Library of Science," asked scientists to boycott journals that did not comply with these demands for open access. Although the proposal was discussed in scientific magazines and conferences, it apparently did not persuade any journal publishers to comply (Young, 2002). Most productive scientists, who work for major universities and research institutes

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Abstract

The aim of this chapter is to demonstrate that semantic issues underlie all research questions within Library and Information Science (LIS, or, as hereafter, IS) and, in particular, the subfield known as Knowledge Organization (KO). Further, it seeks to show that semantics is a field influenced by conflicting views and discusses why it is important to argue for the most fruitful one of these. Moreover, the chapter demonstrates that IS has not yet addressed semantic problems in systematic fashion and examines why the field is very fragmented and without a proper theoretical basis. The focus here is on broad interdisciplinary issues and the long-term perspective. The theoretical problems involving semantics and concepts are very complicated. Therefore, this chapter starts by considering tools developed in KO for information retrieval (IR) as basically semantic tools. In this way, it establishes a specific IS focus on the relation between KO and semantics. It is well known that thesauri consist of a selection of concepts supplemented with information about their semantic relations (such as generic relations or "associative relations"). Some words in thesauri are "preferred terms" (descriptors), whereas others are "lead-in terms." The descriptors represent concepts. The difference between "a word" and "a concept" is that different words may have the same meaning and similar words may have different meanings, whereas one concept expresses one meaning.

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Theme

Citation indexing

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Date

22. 1.2012 13:01:26

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Date

13. 7.2008 9:30:22

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Date

13. 7.2008 19:22:28

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Abstract

Video is a communications medium that normally brings together moving pictures with a synchronized audio track into a discrete piece or pieces of information. A "piece" of video is variously referred to as a frame, a shot, a scene, a Clip, a program, or an episode; these pieces are distinguished by their length and by their composition. We shall return to the definition of each of these in the section an automatically structuring and indexing digital video. In modern society, Video is commonplace and is usually equated with television, movies, or home Video produced by a Video camera or camcorder. We also accept Video recorded from closed circuit TVs for security and surveillance as part of our daily lives. In short, Video is ubiquitous. Digital Video is, as the name suggests, the creation or capture of Video information in digital format. Most Video produced today, commercial, surveillance, or domestic, is produced in digital form, although the medium of Video predates the development of digital computing by several decades. The essential nature of Video has not changed with the advent of digital computing. It is still moving pictures and synchronized audio. However, the production methods and the end product have gone through significant evolution, in the last decade especially.

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Date

10. 9.2000 17:38:22

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10. 9.2000 17:38:22

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10. 9.2000 17:38:22

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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).

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Date

18. 5.2006 20:06:22

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Date

10. 9.2000 17:38:22

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Abstract

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.

Object

Latent Semantic Indexing

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Date

10. 9.2000 17:38:22

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Theme

Citation indexing

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Date

10. 9.2000 17:38:22

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Abstract

As an informational technology, the World Wide Web has enjoyed spectacular success. In just ten years it has transformed the way information is produced, stored, and shared in arenas as diverse as shopping, family photo albums, and high-level academic research. The "Semantic Web" is touted by its developers as equally revolutionary, although it has not yet achieved anything like the Web's exponential uptake. It seeks to transcend a current limitation of the Web - that it largely requires indexing to be accomplished merely on specific character strings. Thus, a person searching for information about "turkey" (the bird) receives from current search engines many irrelevant pages about "Turkey" (the country) and nothing about the Spanish "pavo" even if he or she is a Spanish-speaker able to understand such pages. The Semantic Web vision is to develop technology to facilitate retrieval of information via meanings, not just spellings. For this to be possible, most commentators believe, Semantic Web applications will have to draw on some kind of shared, structured, machine-readable conceptual scheme. Thus, there has been a convergence between the Semantic Web research community and an older tradition with roots in classical Artificial Intelligence (AI) research (sometimes referred to as "knowledge representation") whose goal is to develop a formal ontology. A formal ontology is a machine-readable theory of the most fundamental concepts or "categories" required in order to understand information pertaining to any knowledge domain. A review of the attempts that have been made to realize this goal provides an opportunity to reflect in interestingly concrete ways on various research questions such as the following: - How explicit a machine-understandable theory of meaning is it possible or practical to construct? - How universal a machine-understandable theory of meaning is it possible or practical to construct? - How much (and what kind of) inference support is required to realize a machine-understandable theory of meaning? - What is it for a theory of meaning to be machine-understandable anyway?

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Abstract

This bibliography is not meant to be exhaustive, but unfortunately it is not as complete as I wanted. Some books and articles are not be included, but they may be used in my future work. (These include two books and one article by B.C. Vickery: Faceted Classification Schemes (New Brunswick, NJ: Rutgers, 1966), Classification and Indexing in Science, 3rd ed. (London: Butterworths, 1975), and "Knowledge Representation: A Brief Review" (Journal of Documentation 42 no. 3 (September 1986): 145-159; and A.C. Foskett's "The Future of Faceted Classification" in The Future of Classification, edited by Rita Marcella and Arthur Maltby (Aldershot, England: Gower, 2000): 69-80). Nevertheless, I hope this bibliography will be useful for those both new to or familiar with faceted hypertext systems. Some very basic resources are listed, as well as some very advanced ones. Some example web sites are mentioned, but there is no detailed technical discussion of any software. The user interface to any web site is extremely important, and this is briefly mentioned in two or three places (for example the discussion of lawforwa.org (see Example Web Sites)). The larger question of how to display information graphically and with hypertext is outside the scope of this bibliography. There are five sections: Recommended, Background, Not Relevant, Example Web Sites, and Mailing Lists. Background material is either introductory, advanced, or of peripheral interest, and can be read after the Recommended resources if the reader wants to know more. The Not Relevant category contains articles that may appear in bibliographies but are not relevant for my purposes.