Search (93 results, page 2 of 5)

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Abstract

The emergence of digital libraries (DLs), at the interface of library and information science with computer and communication technologies, helped to expand significantly the literature in all of these areas during the late 1990s. The pace of development is reflected by the number of special issues of major journals in information science and computer science, and the increasing number of workshops and conferences an digital libraries. For example, starting in 1995, the Communications of the ACM has devoted three special issues to the topic (Fox, Akscyn, Furuta, & Leggett, 1995; Fox & Marchionini, 1998, 2001). The Journal of the American Society for Information Science devoted two issues to digital libraries (H. Chen, 2000; Fox & Lunin, 1993); Information Processing & Management and the Journal of Visual Communication and Image Representation each had one special issue (Chen & Fox, 1996; Marchionini & Fox, 1999). The domain of digital libraries, though still evolving, has matured over the last decade, as demonstrated by coverage through D-Lib (http://www.dlib.org), the International Journal an Digital Libraries (http://link.springer.de/link/service/journals/00799), and two overview works (W Y Arms, 2000; Lesk, 1997; both of which have also served as textbooks). Sun Microsystems published a small book to guide those planning a digital library (Noerr, 2000), and IBM has been developing commercial products for digital libraries since 1994 (IBM, 2000). A number of Web sites have extensive sets of pointers to information an DLs (D-Lib Forum, 2001; Fox, 1998a; Habing, 1998; Hein, 2000; Schwartz, 2001a, 2001b). Further, the field has attracted the attention of diverse academics, research groups, and practitionersmany of whom have attended tutorials, workshops, or conferences, e.g., the Joint Conference an Digital Libraries, which is a sequel to a separate series run by ACM and IEEE-CS. Therefore, it is timely that ARIST publishes this first review focusing specifically an digital libraries. There has been no ARIST chapter to date directly dealing with the area of DLs, though some related domains have been covered-particularly: information retrieval, user interfaces (Marchionini & Komlodi, 1998), social informatics of DLs (Bishop & Star, 1996), and scholarly communication (see Borgman and Furner's chapter in this volume). This chapter provides an overview of the diverse aspects and dimensions of DL research, practice, and literature, identifying trends and delineating research directions.

Source

Annual review of information science and technology. 36(2002), S.503-590

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Abstract

This chapter examines the nature of semantic relations and their main applications in information science. The nature and types of semantic relations are discussed from the perspectives of linguistics and psychology. An overview of the semantic relations used in knowledge structures such as thesauri and ontologies is provided, as well as the main techniques used in the automatic extraction of semantic relations from text. The chapter then reviews the use of semantic relations in information extraction, information retrieval, question-answering, and automatic text summarization applications. Concepts and relations are the foundation of knowledge and thought. When we look at the world, we perceive not a mass of colors but objects to which we automatically assign category labels. Our perceptual system automatically segments the world into concepts and categories. Concepts are the building blocks of knowledge; relations act as the cement that links concepts into knowledge structures. We spend much of our lives identifying regular associations and relations between objects, events, and processes so that the world has an understandable structure and predictability. Our lives and work depend on the accuracy and richness of this knowledge structure and its web of relations. Relations are needed for reasoning and inferencing. Chaffin and Herrmann (1988b, p. 290) noted that "relations between ideas have long been viewed as basic to thought, language, comprehension, and memory." Aristotle's Metaphysics (Aristotle, 1961; McKeon, expounded on several types of relations. The majority of the 30 entries in a section of the Metaphysics known today as the Philosophical Lexicon referred to relations and attributes, including cause, part-whole, same and opposite, quality (i.e., attribute) and kind-of, and defined different types of each relation. Hume (1955) pointed out that there is a connection between successive ideas in our minds, even in our dreams, and that the introduction of an idea in our mind automatically recalls an associated idea. He argued that all the objects of human reasoning are divided into relations of ideas and matters of fact and that factual reasoning is founded on the cause-effect relation. His Treatise of Human Nature identified seven kinds of relations: resemblance, identity, relations of time and place, proportion in quantity or number, degrees in quality, contrariety, and causation. Mill (1974, pp. 989-1004) discoursed on several types of relations, claiming that all things are either feelings, substances, or attributes, and that attributes can be a quality (which belongs to one object) or a relation to other objects.
Linguists in the structuralist tradition (e.g., Lyons, 1977; Saussure, 1959) have asserted that concepts cannot be defined on their own but only in relation to other concepts. Semantic relations appear to reflect a logical structure in the fundamental nature of thought (Caplan & Herrmann, 1993). Green, Bean, and Myaeng (2002) noted that semantic relations play a critical role in how we represent knowledge psychologically, linguistically, and computationally, and that many systems of knowledge representation start with a basic distinction between entities and relations. Green (2001, p. 3) said that "relationships are involved as we combine simple entities to form more complex entities, as we compare entities, as we group entities, as one entity performs a process on another entity, and so forth. Indeed, many things that we might initially regard as basic and elemental are revealed upon further examination to involve internal structure, or in other words, internal relationships." Concepts and relations are often expressed in language and text. Language is used not just for communicating concepts and relations, but also for representing, storing, and reasoning with concepts and relations. We shall examine the nature of semantic relations from a linguistic and psychological perspective, with an emphasis on relations expressed in text. The usefulness of semantic relations in information science, especially in ontology construction, information extraction, information retrieval, question-answering, and text summarization is discussed. Research and development in information science have focused on concepts and terms, but the focus will increasingly shift to the identification, processing, and management of relations to achieve greater effectiveness and refinement in information science techniques. Previous chapters in ARIST on natural language processing (Chowdhury, 2003), text mining (Trybula, 1999), information retrieval and the philosophy of language (Blair, 2003), and query expansion (Efthimiadis, 1996) provide a background for this discussion, as semantic relations are an important part of these applications.

Source

Annual review of information science and technology. 40(2006), S.157-228

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Abstract

The preservation of digital objects (defined here as objects in digital form that require a computer to support their existence and display) is obviously an important practical issue for the information professions, with its importance growing daily as more information objects are produced in, or converted to, digital form. Yakel's (2001) review of the field provided a much-needed introduction. At the same time, the complexity of new digital objects continues to increase, challenging existing preservation efforts (Lee, Skattery, Lu, Tang, & McCrary, 2002). The field of information science itself is beginning to pay some reflexive attention to the creation of fragile and unpreservable digital objects. But these concerns focus often an the practical problems of short-term repurposing of digital objects rather than actual preservation, by which I mean the activity of carrying digital objects from one software generation to another, undertaken for purposes beyond the original reasons for creating the objects. For preservation in this sense to be possible, information science as a discipline needs to be active in the formulation of, and advocacy for, national information policies. Such policies will need to challenge the predominant cultural expectation of planned obsolescence for information resources, and cultural artifacts in general.

Form

Elektronische Datenträger

Source

Annual review of information science and technology. 38(2004), S.549-590

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Date

13. 7.2008 19:22:28

Source

Annual review of information science and technology. 40(2006), S.xxx-xxx

Theme

Information

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Abstract

We know this Web world. We live in it, particularly those of us in developed countries. Even if we do not go online daily, we live with itour culture is imprinted with online activity and vocabulary: e-mailing colleagues, surfing the Web, posting Web pages, blogging, gender-bending in cyberspace, texting and instant messaging friends, engaging in ecommerce, entering an online chat room, or morphing in an online world. We use it-to conduct business, find information, talk with friends and colleagues. We know it is something separate, yet we incorporate it into our daily lives. We identify with it, bringing to it behaviors and expectations we hold for the world in general. We approach it as explorers and entrepreneurs, ready to move into unknown opportunities and territory; creators and engineers, eager to build new structures; utopians for whom "the world of the Web" represents the chance to start again and "get it right" this time; utilitarians, ready to get what we can out of the new structures; and dystopians, for whom this is just more evidence that there is no way to "get it right." The word "world" has many connotations. The Oxford English Dictionary (http://dictionary.oed.com) gives 27 definitions for the noun "world" including: - The sphere within which one's interests are bound up or one's activities find scope; (one's) sphere of action or thought; the "realm" within which one moves or lives. - A group or system of things or beings associated by common characteristics (denoted by a qualifying word or phrase), or considered as constituting a unity. - Human society considered in relation to its activities, difficulties, temptations, and the like; hence, contextually, the ways, practices, or customs of the people among whom one lives; the occupations and interests of society at large.

Source

Annual review of information science and technology. 39(2005), S.311-346

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Abstract

The challenge of cataloging Web sites and electronic resources was the most important issue facing the cataloging world in the last year. This article reviews attempts to analyze and revise the cataloging code in view of the new electronic environment. The difficulties of applying traditional library cataloging standards to Web resources has led some to favor metadata as the best means of providing access to these materials. The appropriate education and training for library cataloging personnel remains crucial during this transitional period. Articles on user understanding of Library of Congress subject headings and on cataloging practice are also reviewed.

Date

10. 9.2000 17:38:22

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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

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Abstract

Imagine a world where you walk up to a computer and sing the song fragment that has been plaguing you since breakfast. The computer accepts your off-key singing, corrects your request, and promptly suggests to you that "Camptown Races" is the cause of your irritation. You confirm the computer's suggestion by listening to one of the many MP3 files it has found. Satisfied, you kindly decline the offer to retrieve all extant versions of the song, including a recently released Italian rap rendition and an orchestral score featuring a bagpipe duet. Does such a system exist today? No. Will it in the future? Yes. Will such a system be easy to produce? Most decidedly not. Myriad difficulties remain to be overcome before the creation, deployment, and evaluation of robust, large-scale, and content-based Music Information Retrieval (MIR) systems become reality. The dizzyingly complex interaction of music's pitch, temporal, harmonic, timbral, editorial, textual, and bibliographic "facets," for example, demonstrates just one of MIR's perplexing problems. The choice of music representation-whether symbol-based, audio-based, or both-further compounds matters, as each choice determines bandwidth, computation, storage, retrieval, and interface requirements and capabilities.

Source

Annual review of information science and technology. 37(2003), S.295-342

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Date

13. 7.2008 9:30:22

Source

Annual review of information science and technology. 42(2008), S.xxx-xxx

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Date

13. 7.2008 19:53:22

Source

Annual review of information science and technology. 41(2007), S.xxx-xxx

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Abstract

What is an electronic record, how should it best be preserved and made available, and to what extent do traditional, paradigmatic archival precepts such as provenance, original order, and archival custody hold when managing it? Over more than four decades of work in the area of electronic records (formerly known as machine-readable records), theorists and researchers have offered answers to these questions-or at least devised approaches for trying to answer them. However, a set of fundamental questions about the nature of the record and the applicability of traditional archival theory still confronts researchers seeking to advance knowledge and development in this increasingly active, but contested, area of research. For example, which characteristics differentiate a record from other types of information objects (such as publications or raw research data)? Are these characteristics consistently present regardless of the medium of the record? Does the record always have to have a tangible form? How does the record manifest itself within different technological and procedural contexts, and in particular, how do we determine the parameters of electronic records created in relational, distributed, or dynamic environments that bear little resemblance an the surface to traditional paper-based environments? At the heart of electronic records research lies a dual concern with the nature of the record as a specific type of information object and the nature of legal and historical evidence in a digital world. Electronic records research is relevant to the agendas of many communities in addition to that of archivists. Its emphasis an accountability and an establishing trust in records, for example, addresses concerns that are central to both digital government and e-commerce. Research relating to electronic records is still relatively homogeneous in terms of scope, in that most major research initiatives have addressed various combinations of the following: theory building in terms of identifying the nature of the electronic record, developing alternative conceptual models, establishing the determinants of reliability and authenticity in active and preserved electronic records, identifying functional and metadata requirements for record keeping, developing and testing preservation

Form

Elektronische Dokumente

Source

Annual review of information science and technology. 39(2005), S.219-256

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Source

Bibliothek: Forschung und Praxis. 33(2009) H.3, S.379-384

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Abstract

Advanced technology has resulted in the generation of about one million terabytes of information every year. Ninety-reine percent of this is available in digital format (Keim, 2001). More information will be generated in the next three years than was created during all of previous human history (Keim, 2001). Collecting information is no longer a problem, but extracting value from information collections has become progressively more difficult. Various search engines have been developed to make it easier to locate information of interest, but these work well only for a person who has a specific goal and who understands what and how information is stored. This usually is not the Gase. Visualization was commonly thought of in terms of representing human mental processes (MacEachren, 1991; Miller, 1984). The concept is now associated with the amplification of these mental processes (Card, Mackinlay, & Shneiderman, 1999). Human eyes can process visual cues rapidly, whereas advanced information analysis techniques transform the computer into a powerful means of managing digitized information. Visualization offers a link between these two potent systems, the human eye and the computer (Gershon, Eick, & Card, 1998), helping to identify patterns and to extract insights from large amounts of information. The identification of patterns is important because it may lead to a scientific discovery, an interpretation of clues to solve a crime, the prediction of catastrophic weather, a successful financial investment, or a better understanding of human behavior in a computermediated environment. Visualization technology shows considerable promise for increasing the value of large-scale collections of information, as evidenced by several commercial applications of TreeMap (e.g., http://www.smartmoney.com) and Hyperbolic tree (e.g., http://www.inxight.com) to visualize large-scale hierarchical structures. Although the proliferation of visualization technologies dates from the 1990s where sophisticated hardware and software made increasingly faster generation of graphical objects possible, the role of visual aids in facilitating the construction of mental images has a long history. Visualization has been used to communicate ideas, to monitor trends implicit in data, and to explore large volumes of data for hypothesis generation. Imagine traveling to a strange place without a map, having to memorize physical and chemical properties of an element without Mendeleyev's periodic table, trying to understand the stock market without statistical diagrams, or browsing a collection of documents without interactive visual aids. A collection of information can lose its value simply because of the effort required for exhaustive exploration. Such frustrations can be overcome by visualization.
Visualization can be classified as scientific visualization, software visualization, or information visualization. Although the data differ, the underlying techniques have much in common. They use the same elements (visual cues) and follow the same rules of combining visual cues to deliver patterns. They all involve understanding human perception (Encarnacao, Foley, Bryson, & Feiner, 1994) and require domain knowledge (Tufte, 1990). Because most decisions are based an unstructured information, such as text documents, Web pages, or e-mail messages, this chapter focuses an the visualization of unstructured textual documents. The chapter reviews information visualization techniques developed over the last decade and examines how they have been applied in different domains. The first section provides the background by describing visualization history and giving overviews of scientific, software, and information visualization as well as the perceptual aspects of visualization. The next section assesses important visualization techniques that convert abstract information into visual objects and facilitate navigation through displays an a computer screen. It also explores information analysis algorithms that can be applied to identify or extract salient visualizable structures from collections of information. Information visualization systems that integrate different types of technologies to address problems in different domains are then surveyed; and we move an to a survey and critique of visualization system evaluation studies. The chapter concludes with a summary and identification of future research directions.

Source

Annual review of information science and technology. 39(2005), S.139-177

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Abstract

Relevance is a, if not even the, key notion in information science in general and information retrieval in particular. This two-part critical review traces and synthesizes the scholarship on relevance over the past 30 years and provides an updated framework within which the still widely dissonant ideas and works about relevance might be interpreted and related. It is a continuation and update of a similar review that appeared in 1975 under the same title, considered here as being Part I. The present review is organized into two parts: Part II addresses the questions related to nature and manifestations of relevance, and Part III addresses questions related to relevance behavior and effects. In Part II, the nature of relevance is discussed in terms of meaning ascribed to relevance, theories used or proposed, and models that have been developed. The manifestations of relevance are classified as to several kinds of relevance that form an interdependent system of relevances. In Part III, relevance behavior and effects are synthesized using experimental and observational works that incorporate data. In both parts, each section concludes with a summary that in effect provides an interpretation and synthesis of contemporary thinking on the topic treated or suggests hypotheses for future research. Analyses of some of the major trends that shape relevance work are offered in conclusions.

Content

Relevant: Having significant and demonstrable bearing on the matter at hand.[Note *][A version of this article has been published in 2006 as a chapter in E.G. Abels & D.A. Nitecki (Eds.), Advances in Librarianship (Vol. 30, pp. 3-71). San Diego: Academic Press. (Saracevic, 2006).] Relevance: The ability as of an information retrieval system to retrieve material that satisfies the needs of the user. - Merriam-Webster Dictionary 2005

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.13, S.1915-1933

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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.

Source

Annual review of information science and technology. 41(2007), S.367-405

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Abstract

This paper reviews the enormous changes in cataloging and classification reflected in the literature of 2003 and 2004, and discusses major themes and issues. Traditional cataloging and classification tools have been re-vamped and new resources have emerged. Most notable themes are: the continuing influence of the Functional Requirements for Bibliographic Control (FRBR); the struggle to understand the ever-broadening concept of an "information entity"; steady developments in metadata-encoding standards; and the globalization of information systems, including multilinguistic challenges.

Date

10. 9.2000 17:38:22

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Abstract

This chapter reviews visualization techniques that can be used to map the ever-growing domain structure of scientific disciplines and to support information retrieval and classification. In contrast to the comprehensive surveys conducted in traditional fashion by Howard White and Katherine McCain (1997, 1998), this survey not only reviews emerging techniques in interactive data analysis and information visualization, but also depicts the bibliographical structure of the field itself. The chapter starts by reviewing the history of knowledge domain visualization. We then present a general process flow for the visualization of knowledge domains and explain commonly used techniques. In order to visualize the domain reviewed by this chapter, we introduce a bibliographic data set of considerable size, which includes articles from the citation analysis, bibliometrics, semantics, and visualization literatures. Using tutorial style, we then apply various algorithms to demonstrate the visualization effectsl produced by different approaches and compare the results. The domain visualizations reveal the relationships within and between the four fields that together constitute the focus of this chapter. We conclude with a general discussion of research possibilities. Painting a "big picture" of scientific knowledge has long been desirable for a variety of reasons. Traditional approaches are brute forcescholars must sort through mountains of literature to perceive the outlines of their field. Obviously, this is time-consuming, difficult to replicate, and entails subjective judgments. The task is enormously complex. Sifting through recently published documents to find those that will later be recognized as important is labor intensive. Traditional approaches struggle to keep up with the pace of information growth. In multidisciplinary fields of study it is especially difficult to maintain an overview of literature dynamics. Painting the big picture of an everevolving scientific discipline is akin to the situation described in the widely known Indian legend about the blind men and the elephant. As the story goes, six blind men were trying to find out what an elephant looked like. They touched different parts of the elephant and quickly jumped to their conclusions. The one touching the body said it must be like a wall; the one touching the tail said it was like a snake; the one touching the legs said it was like a tree trunk, and so forth. But science does not stand still; the steady stream of new scientific literature creates a continuously changing structure. The resulting disappearance, fusion, and emergence of research areas add another twist to the tale-it is as if the elephant is running and dynamically changing its shape. Domain visualization, an emerging field of study, is in a similar situation. Relevant literature is spread across disciplines that have traditionally had few connections. Researchers examining the domain from a particular discipline cannot possibly have an adequate understanding of the whole. As noted by White and McCain (1997), the new generation of information scientists is technically driven in its efforts to visualize scientific disciplines. However, limited progress has been made in terms of connecting pioneers' theories and practices with the potentialities of today's enabling technologies. If the difference between past and present generations lies in the power of available technologies, what they have in common is the ultimate goal-to reveal the development of scientific knowledge.

Source

Annual review of information science and technology. 37(2003), S.179-258

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Theme

Klassifikationssysteme im Online-Retrieval
Verbale Doksprachen im Online-Retrieval

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Abstract

This review of 2004 and 2005 serials literature covers the themes of cost, management, and access. Interwoven through the serials literature of these two years are the importance of collaboration, communication, and linkages between scholars, publishers, subscription agents and other intermediaries, and librarians. The emphasis in the literature is on electronic serials and their impact on publishing, libraries, and vendors. In response to the crisis of escalating journal prices and libraries' dissatisfaction with the Big Deal licensing agreements, Open Access journals and publishing models were promoted. Libraries subscribed to or licensed increasing numbers of electronic serials. As a result, libraries sought ways to better manage licensing and subscription data (not handled by traditional integrated library systems) by implementing electronic resources management systems. In order to provide users with better, faster, and more current information on and access to electronic serials, libraries implemented tools and services to provide A-Z title lists, title by title coverage data, MARC records, and OpenURL link resolvers.

Date

10. 9.2000 17:38:22

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Source

Annual review of information science and technology. 42(2008), S.xxx-xxx

Theme

Information