Search (32 results, page 1 of 2)

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Abstract

The different points of views an knowledge representation and organization from various research communities reflect underlying philosophies and paradigms in these communities. This paper reviews differences and relations in knowledge representation and organization and generalizes four paradigms-integrative and disintegrative pragmatism and integrative and disintegrative epistemologism. Examples such as classification, XML schemas, and ontologies are compared based an how they specify concepts, build data models, and encode knowledge organization structures. 1. Introduction Knowledge representation (KR) is a term that several research communities use to refer to somewhat different aspects of the same research area. The artificial intelligence (AI) community considers KR as simply "something to do with writing down, in some language or communications medium, descriptions or pictures that correspond in some salient way to the world or a state of the world" (Duce & Ringland, 1988, p. 3). It emphasizes the ways in which knowledge can be encoded in a computer program (Bench-Capon, 1990). For the library and information science (LIS) community, KR is literally the synonym of knowledge organization, i.e., KR is referred to as the process of organizing knowledge into classifications, thesauri, or subject heading lists. KR has another meaning in LIS: it "encompasses every type and method of indexing, abstracting, cataloguing, classification, records management, bibliography and the creation of textual or bibliographic databases for information retrieval" (Anderson, 1996, p. 336). Adding the social dimension to knowledge organization, Hjoerland (1997) states that knowledge is a part of human activities and tied to the division of labor in society, which should be the primary organization of knowledge. Knowledge organization in LIS is secondary or derived, because knowledge is organized in learned institutions and publications. These different points of views an KR suggest that an essential difference in the understanding of KR between both AI and LIS lies in the source of representationwhether KR targets human activities or derivatives (knowledge produced) from human activities. This difference also decides their difference in purpose-in AI KR is mainly computer-application oriented or pragmatic and the result of representation is used to support decisions an human activities, while in LIS KR is conceptually oriented or abstract and the result of representation is used for access to derivatives from human activities.

Date

12. 9.2004 17:22:35

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RSWK

Art / Evolution / Aufsatzsammlung (BVB)

Subject

Art / Evolution / Aufsatzsammlung (BVB)

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Source

International journal of human-computer studies. 44(1996) no.5, S.603-627

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Abstract

Logic and the Organization of Information closely examines the historical and contemporary methodologies used to catalogue information objects-books, ebooks, journals, articles, web pages, images, emails, podcasts and more-in the digital era. This book provides an in-depth technical background for digital librarianship, and covers a broad range of theoretical and practical topics including: classification theory, topic annotation, automatic clustering, generalized synonymy and concept indexing, distributed libraries, semantic web ontologies and Simple Knowledge Organization System (SKOS). It also analyzes the challenges facing today's information architects, and outlines a series of techniques for overcoming them. Logic and the Organization of Information is intended for practitioners and professionals working at a design level as a reference book for digital librarianship. Advanced-level students, researchers and academics studying information science, library science, digital libraries and computer science will also find this book invaluable.

LCSH

Computer science

Subject

Computer science

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Source

Classification theory in the computer age: conversations across the disciplines. Proc. from the Conf. 18.-19.11.1988, Albany, NY

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Date

1. 8.1996 22:01:00

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Date

1. 8.1996 22:01:00

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Date

6. 5.2017 18:46:22

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Abstract

One of the main topics of scientific research, classification is the operation consisting of distributing objects in classes or groups which are, in general, less numerous than them. From Antiquity to the Classical Age, it has a long history where philosophers (Aristotle), and natural scientists (Linnaeus), took a great part. But from the nineteenth century (with the growth of chemistry and information science) and the twentieth century (with the arrival of mathematical models and computer science), mathematics (especially theory of orders and theory of graphs or hypergraphs) allows us to compute all the possible partitions, chains of partitions, covers, hypergraphs or systems of classes we can construct on a domain. In spite of these advances, most of classifications are still based on the evaluation of ressemblances between objects that constitute the empirical data. However, all these classifications remain, for technical and epistemological reasons we detail below, very unstable ones. We lack a real algebra of classifications, which could explain their properties and the relations existing between them. Though the aim of a general theory of classifications is surely a wishful thought, some recent conjecture gives the hope that the existence of a metaclassification (or classification of all classification schemes) is possible

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Footnote

Rez. in: KO 36(2009) no.1, S.62-63 (K. La Barre): "This special issue of Axiomathes presents an ambitious dual agenda. It attempts to highlight aspects of facet analysis (as used in LIS) that are shared by cognate approaches in philosophy, psychology, linguistics and computer science. Secondarily, the issue aims to attract others to the study and use of facet analysis. The authors represent a blend of lifetime involvement with facet analysis, such as Vickery, Broughton, Beghtol, and Dahlberg; those with well developed research agendas such as Tudhope, and Priss; and relative newcomers such as Gnoli, Cheti and Paradisi, and Slavic. Omissions are inescapable, but a more balanced issue would have resulted from inclusion of at least one researcher from the Indian school of facet theory. Another valuable addition might have been a reaction to the issue by one of the chief critics of facet analysis. Potentially useful, but absent, is a comprehensive bibliography of resources for those wishing to engage in further study, that now lie scattered throughout the issue. Several of the papers assume relative familiarity with facet analytical concepts and definitions, some of which are contested even within LIS. Gnoli's introduction (p. 127-130) traces the trajectory, extensions and new developments of this analytico- synthetic approach to subject access, while providing a laundry list of cognate approaches that are similar to facet analysis. This brief essay and the article by Priss (p. 243-255) directly addresses this first part of Gnoli's agenda. Priss provides detailed discussion of facet-like structures in computer science (p. 245- 246), and outlines the similarity between Formal Concept Analysis and facets. This comparison is equally fruitful for researchers in computer science and library and information science. By bridging into a discussion of visualization challenges for facet display, further research is also invited. Many of the remaining papers comprehensively detail the intellectual heritage of facet analysis (Beghtol; Broughton, p. 195-198; Dahlberg; Tudhope and Binding, p. 213-215; Vickery). Beghtol's (p. 131-144) examination of the origins of facet theory through the lens of the textbooks written by Ranganathan's mentor W.C.B. Sayers (1881-1960), Manual of Classification (1926, 1944, 1955) and a textbook written by Mills A Modern Outline of Classification (1964), serves to reveal the deep intellectual heritage of the changes in classification theory over time, as well as Ranganathan's own influence on and debt to Sayers.

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Abstract

Special classifications have been somewhat neglected in KO compared to general classifications. The methodology of constructing special classifications is important, however, also for the methodology of constructing general classification schemes. The methodology of constructing special classifications can be regarded as one among about a dozen approaches to domain analysis. The methodology of (special) classification in LIS has been dominated by the rationalistic facet-analytic tradition, which, however, neglects the question of the empirical basis of classification. The empirical basis is much better grasped by, for example, bibliometric methods. Even the combination of rational and empirical methods is insufficient. This presentation will provide evidence for the necessity of historical and pragmatic methods for the methodology of classification and will point to the necessity of analyzing "paradigms". The presentation covers the methods of constructing classifications from Ranganathan to the design of ontologies in computer science and further to the recent "paradigm shift" in classification research. 1. Introduction Classification of a subject field is one among about eleven approaches to analyzing a domain that are specific for information science and in my opinion define the special competencies of information specialists (Hjoerland, 2002a). Classification and knowledge organization are commonly regarded as core qualifications of librarians and information specialists. Seen from this perspective one expects a firm methodological basis for the field. This paper tries to explore the state-of-the-art conceming the methodology of classification. 2. Classification: Science or non-science? As it is part of the curriculum at universities and subject in scientific journals and conferences like ISKO, orte expects classification/knowledge organization to be a scientific or scholarly activity and a scientific field. However, very often when information specialists classify or index documents and when they revise classification system, the methods seem to be rather ad hoc. Research libraries or scientific databases may employ people with adequate subject knowledge. When information scientists construct or evaluate systems, they very often elicit the knowledge from "experts" (Hjorland, 2002b, p. 260). Mostly no specific arguments are provided for the specific decisions in these processes.

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Date

22. 4.1997 21:10:19

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Source

Bulletin d'informations de l'Association des Bibliothecaires Francais. 1997, no.175, S.22-23

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Date

27. 5.2007 22:19:35

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Pages

S.1-22

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Footnote

Paper presented at conference on 'Local documents, a new classification scheme' at the Research Caucus of the Florida Library Association Annual Conference, Fort Lauderdale, Florida 22 Apr 95

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Date

10. 9.2000 17:38:22

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Pages

S.22-36

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Source

Library trends. 48(1999) no.1, S.22-47

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Date

22.12.2007 17:22:31