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  1. Liu, X.; Qin, J.: ¬An interactive metadata model for structural, descriptive, and referential representation of scholarly output (2014) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.5, S.964-983
  2. Ya-Ning, C.; Hao-Ren, K.: FRBRoo-based approach to heterogeneous metadata integration (2013) 0.00
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    Source
    Journal of documentation. 69(2013) no.5, S.623-637
  3. Hider, P.: ¬A survey of the coverage and methodologies of schemas and vocabularies used to describe information resources (2015) 0.00
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    Source
    Knowledge organization. 42(2015) no.3, S.154-163
  4. White, M.: ¬The value of taxonomies, thesauri and metadata in enterprise search (2016) 0.00
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    Source
    Knowledge organization. 43(2016) no.3, S.184-192
  5. Husevag, A.-S.R.: Named entities in indexing : a case study of TV subtitles and metadata records (2016) 0.00
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    Pages
    S.48-58
  6. Fidler, B.; Acker, A.: Metadata, infrastructure, and computer-mediated communication in historical perspective (2017) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.2, S.412-422
  7. Gursoy, A.; Wickett, K.; Feinberg, M.: Understanding tag functions in a moderated, user-generated metadata ecosystem (2018) 0.00
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    Source
    Journal of documentation. 74(2018) no.3, S.490-508
  8. Çelebi, A.; Özgür, A.: Segmenting hashtags and analyzing their grammatical structure (2018) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 69(2018) no.5, S.675-686
  9. Tallerås, C.; Dahl, J.H.B.; Pharo, N.: User conceptualizations of derivative relationships in the bibliographic universe (2018) 0.00
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    Source
    Journal of documentation. 74(2018) no.4, S.894-916
  10. Bogaard, T.; Hollink, L.; Wielemaker, J.; Ossenbruggen, J. van; Hardman, L.: Metadata categorization for identifying search patterns in a digital library (2019) 0.00
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    Source
    Journal of documentation. 75(2019) no.2, S.270-286
  11. Montenegro, M.: Subverting the universality of metadata standards (2019) 0.00
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    Source
    Journal of documentation. 75(2019) no.4, S.731-749
  12. Hansson, K.; Dahlgren, A.: Open research data repositories : practices, norms, and metadata for sharing images (2022) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.2, S.303-316
  13. Qin, C.; Liu, Y.; Ma, X.; Chen, J.; Liang, H.: Designing for serendipity in online knowledge communities : an investigation of tag presentation formats and openness to experience (2022) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.10, S.1401-1417
  14. Pole, T.: Contextual classification in the Metadata Object Manager (M.O.M.) (1999) 0.00
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    Abstract
    To Classify is (according to Webster's) "to distribute into classes; to arrange according to a system; to arrange in sets according to some method founded on common properties or characters." A model of classification is a type or category or (excuse the recursive definition) a class of classification "system" as mentioned in Webster's definition. One employs a classification model to implement a specific classification system. (E.g. we employ the hierarchical classification model to implement the Dewey Decimal System) An effective classification model must represent both the commonality (Webster's "common properties"), and also the differences among the items being classified. The commonality of each category or class defines a test to determine which items belong to the set that class represents. The relationships among the classes define the variability among the sets that the classification model can represent. Therefore, a classification model is more than an enumeration or other simple listing of the names of its classes. Our purpose in employing classification models is to build metadata systems that represent and manage knowledge, so that users of these systems we build can: quickly and accurately define (the commonality of) what knowledge they require, allowing the user great flexibility in how that desire is described; be presented existing information assets that best match the stated requirements; distinguish (the variability) among the candidates to determine their best choice(s), without actually having to examine the individual items themselves; retrieve the knowledge they need The MetaData model we present is Contextual Classification. It is a synthesis of several traditional metadata models, including controlled keyword indices, hierarchical classification, attribute value systems, Faceted Classification, and Evolutionary Faceted Classification. Research into building on line library systems of software and software documentation (Frakes and Pole, 19921 and Pole 19962) has shown the need and viability of combining the strengths, and minimizing the weaknesses of multiple metadata models in the development of information systems. The MetaData Object Manager (M.O.M.), a MetaData Warehouse (MDW) and editorial work flow system developed for the Thomson Financial Publishing Group, builds on this earlier research. From controlled keyword systems we borrow the idea of representing commonalties by defining formally defined subject areas or categories of information, which sets are represented by these categories names. From hierarchical classification, we borrow the concept of relating these categories and classes to each other to represent the variability in a collection of information sources. From attribute value we borrow the concept that each information source can be described in different ways, each in respect to the attribute of the information being described. From Faceted Classification we borrow the concept of relating the classes themselves into sets of classes, which a faceted classification system would describe as facets of terms. In this paper we will define the Contextual Classification model, comparing it to the traditional metadata models from which it has evolved. Using the MOM as an example, we will then discuss both the use of Contextual Classification is developing this system, and the organizational, performance and reliability
    Pages
    S.194-202
  15. Jun, W.: ¬A knowledge network constructed by integrating classification, thesaurus and metadata in a digital library (2003) 0.00
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    Source
    Bulletin of the American Society for Information Science. 29(2003) no.2, S.24-28
  16. Aldana, J.F.; Gómez, A.C.; Moreno, N.; Nebro, A.J.; Roldán, M.M.: Metadata functionality for semantic Web integration (2003) 0.00
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    Pages
    S.298-304
  17. Schroeder, K.: Persistent Identifiers im Kontext der Langzeitarchivierung : EPICUR auf dem 2. Bibliothekskongress in Leipzig (2004) 0.00
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    Source
    Dialog mit Bibliotheken. 16(2004) H.2, S.11-14
  18. Cwiok, J.: ¬The defining element : a discussion of the creator element within metadata schemas (2005) 0.00
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    Source
    Cataloging and classification quarterly. 40(2005) nos.3/4, S.103-133
  19. Gracy, K.F.: Enriching and enhancing moving images with Linked Data : an exploration in the alignment of metadata models (2018) 0.00
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    Source
    Journal of documentation. 74(2018) no.2, S.354-371
  20. Alemu, G.: ¬A theory of metadata enriching and filtering (2016) 0.00
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    Source
    Libri. 66(2016) no.4, S.251-262

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