Search (10 results, page 1 of 1)

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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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Date

27. 5.2007 22:19:35

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Date

10. 9.2000 17:38:22

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Date

22.12.2007 17:22:31

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Pages

S.19-22

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Abstract

1. Introduction Theoretical context independent explanations of classification could enhance the universality of classification research and make knowledge about classification available to settings other than traditional libraries. There is a tremendous need for constructing classificatory structures in a range of settings many of which are far removed from the environment in which classification theory and research has been practiced in the last century and a half. The construction of classificatory structures an the Internet, intranets, and in knowledge management systems has received some attention lately. The question examined here is whether it is possible to create a single theory of classification that applies to the range of contexts in which classificatory structures are applied. The object of this paper is to question the assumption that bibliographic classification theory can resemble scientific theories. It is argued that the context of any classification influences the use and understanding of the classification to such a degree that the classification cannot be understood separate from its context. Furthermore, the development from being a novice classifier or classificationist to becoming an expert is explored. lt is assumed scientific theories must relate as much to the activity of novices as to the activity of experts and that scientific theories explain both what it is that novices do and what experts do. It is argued that expertise is achieved not through a correct application of a classification theory but through experiences and adjustment to a particular context and that the activities of novices are quite distinct from the activities of experts in that experts draws an the context of the situation and that novices do not. 2. Theory of Classification Langridge (1976) provides an account of the principles of constructing knowledge organization systems and the theoretical underpinnings of different approaches. He identifies four principles that have guided construction of knowledge organization systems: 1) ideological, 2) social purpose, 3) scientific, and 4) the disciplines. The ideological principle organizes knowledge according to an ideology that the knowledge organization system serves. Langridge gives the examples of "the Christian schemes of the Middle Ages and the Soviet scheme which substitutes for the Bible and Christianity the works of Marx and Lenin and the 'religion' of communism" (Langridge, 1976, p. 4-5).

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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

7.11.2008 15:22:04