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  • × author_ss:"Qin, J."
  • × year_i:[2000 TO 2010}
  1. Qin, J.: Evolving paradigms of knowledge representation and organization : a comparative study of classification, XML/DTD and ontology (2003) 0.02
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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
  2. Qin, J.; Paling, S.: Converting a controlled vocabulary into an ontology : the case of GEM (2001) 0.01
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    Date
    24. 8.2005 19:20:22
  3. Qin, J.: Semantic similarities between a keyword database and a controlled vocabulary database : an investigation in the antibiotic resistance literature (2000) 0.01
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    Abstract
    The 'KeyWords Plus' in the Science Citation Index database represents an approach to combining citation and semantic indexing in describing the document content. This paper explores the similariites or dissimilarities between citation-semantic and analytic indexing. The dataset consisted of over 400 matching records in the SCI and MEDLINE databases on antibiotic resistance in pneumonia. The degree of similarity in indexing terms was found to vary on a scale from completely different to completely identical with various levels in between. The within-document similarity in the 2 databases was measured by a variation on the Jaccard coefficient - the Inclusion Index. The average inclusion coefficient was 0,4134 for SCI and 0,3371 for Medline. The 20 terms occuring most frequently in each database were identified. The 2 groups of terms shared the same terms that consist of the 'intellectual base' for the subject. conceptual similarity was analyzed through scatterplots of matching and nonmatching terms vs. partially identical and broader/narrower terms. The study also found that both databases differed in assigning terms in various semantic categories. Implications of this research and further studies are suggested
  4. Qin, J.; Chen, J.: ¬A multi-layered, multi-dimensional representation of digital educational resources (2003) 0.01
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    Source
    Subject retrieval in a networked environment: Proceedings of the IFLA Satellite Meeting held in Dublin, OH, 14-16 August 2001 and sponsored by the IFLA Classification and Indexing Section, the IFLA Information Technology Section and OCLC. Ed.: I.C. McIlwaine
  5. Chen, M.; Liu, X.; Qin, J.: Semantic relation extraction from socially-generated tags : a methodology for metadata generation (2008) 0.01
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    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas