Search (8 results, page 1 of 1)

  • × type_ss:"a"
  • × author_ss:"Mitchell, J.S."
  1. Mitchell, J.S.; Zeng, M.L.; Zumer, M.: Modeling classification systems in multicultural and multilingual contexts (2012) 0.04
    0.04449667 = product of:
      0.08899334 = sum of:
        0.062076043 = weight(_text_:data in 1967) [ClassicSimilarity], result of:
          0.062076043 = score(doc=1967,freq=8.0), product of:
            0.14807065 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.046827413 = queryNorm
            0.4192326 = fieldWeight in 1967, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.046875 = fieldNorm(doc=1967)
        0.0269173 = product of:
          0.0538346 = sum of:
            0.0538346 = weight(_text_:22 in 1967) [ClassicSimilarity], result of:
              0.0538346 = score(doc=1967,freq=4.0), product of:
                0.16398162 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046827413 = queryNorm
                0.32829654 = fieldWeight in 1967, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=1967)
          0.5 = coord(1/2)
      0.5 = coord(2/4)
    
    Abstract
    This paper reports on the second part of an initiative of the authors on researching classification systems with the conceptual model defined by the Functional Requirements for Subject Authority Data (FRSAD) final report. In an earlier study, the authors explored whether the FRSAD conceptual model could be extended beyond subject authority data to model classification data. The focus of the current study is to determine if classification data modeled using FRSAD can be used to solve real-world discovery problems in multicultural and multilingual contexts. The paper discusses the relationships between entities (same type or different types) in the context of classification systems that involve multiple translations and /or multicultural implementations. Results of two case studies are presented in detail: (a) two instances of the DDC (DDC 22 in English, and the Swedish-English mixed translation of DDC 22), and (b) Chinese Library Classification. The use cases of conceptual models in practice are also discussed.
  2. Mitchell, J.S.; Zeng, M.L.; Zumer, M.: Modeling classification systems in multicultural and multilingual contexts (2014) 0.04
    0.03708056 = product of:
      0.07416112 = sum of:
        0.05173004 = weight(_text_:data in 1962) [ClassicSimilarity], result of:
          0.05173004 = score(doc=1962,freq=8.0), product of:
            0.14807065 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.046827413 = queryNorm
            0.34936053 = fieldWeight in 1962, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1962)
        0.022431081 = product of:
          0.044862162 = sum of:
            0.044862162 = weight(_text_:22 in 1962) [ClassicSimilarity], result of:
              0.044862162 = score(doc=1962,freq=4.0), product of:
                0.16398162 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046827413 = queryNorm
                0.27358043 = fieldWeight in 1962, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=1962)
          0.5 = coord(1/2)
      0.5 = coord(2/4)
    
    Abstract
    This article reports on the second part of an initiative of the authors on researching classification systems with the conceptual model defined by the Functional Requirements for Subject Authority Data (FRSAD) final report. In an earlier study, the authors explored whether the FRSAD conceptual model could be extended beyond subject authority data to model classification data. The focus of the current study is to determine if classification data modeled using FRSAD can be used to solve real-world discovery problems in multicultural and multilingual contexts. The article discusses the relationships between entities (same type or different types) in the context of classification systems that involve multiple translations and/or multicultural implementations. Results of two case studies are presented in detail: (a) two instances of the Dewey Decimal Classification [DDC] (DDC 22 in English, and the Swedish-English mixed translation of DDC 22), and (b) Chinese Library Classification. The use cases of conceptual models in practice are also discussed.
  3. Mitchell, J.S.; Zeng, M.L.; Zumer, M.: Extending models for controlled vocabularies to classification systems : modeling DDC with FRSAD (2011) 0.01
    0.014458986 = product of:
      0.057835944 = sum of:
        0.057835944 = weight(_text_:data in 4092) [ClassicSimilarity], result of:
          0.057835944 = score(doc=4092,freq=10.0), product of:
            0.14807065 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.046827413 = queryNorm
            0.39059696 = fieldWeight in 4092, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4092)
      0.25 = coord(1/4)
    
    Abstract
    The Functional Requirements for Subject Authority Data (FRSAD) conceptual model identifies entities, attributes and relationships as they relate to subject authority data. FRSAD includes two main entities, thema (any entity used as a subject of a work) and nomen (any sign or sequence of signs that a thema is known by, referred to, or addressed as). In a given controlled vocabulary and within a domain, a nomen is the appellation of only one thema. The authors consider the question, can the FRSAD conceptual model be extended beyond controlled vocabularies (its original focus) to model classification data? Models that are developed based on the structures and functions of controlled vocabularies (such as thesauri and subject heading systems) often need to be adjusted or extended to accommodate classification systems that have been developed with different focused functions, structures and fundamental theories. The Dewey Decimal Classification (DDC) system is used as a case study to test applicability of the FRSAD model for classification data, and as a springboard for a general discussion of issues related to the use of FRSAD for the representation of classification data.
  4. Mitchell, J.S.; Zeng, M.L.; Zumer, M.: Extending models for controlled vocabularies to classification systems : modelling DDC with FRSAD (2011) 0.01
    0.014458986 = product of:
      0.057835944 = sum of:
        0.057835944 = weight(_text_:data in 4828) [ClassicSimilarity], result of:
          0.057835944 = score(doc=4828,freq=10.0), product of:
            0.14807065 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.046827413 = queryNorm
            0.39059696 = fieldWeight in 4828, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4828)
      0.25 = coord(1/4)
    
    Abstract
    The Functional Requirements for Subject Authority Data (FRSAD) conceptual model identifies entities, attributes and relationships as they relate to subject authority data. FRSAD includes two main entities, thema (any entity used as a subject of a work) and nomen (any sign or sequence of signs that a thema is known by, referred to, or addressed as). In a given controlled vocabulary and within a domain, a nomen is the appellation of only one thema. The authors consider the question, can the FRSAD conceptual model be extended beyond controlled vocabularies (its original focus) to model classification data? Models that are developed based on the structures and functions of controlled vocabularies (such as thesauri and subject heading systems) often need to be adjusted or extended to accommodate classification systems that have been developed with different focused functions, structures and fundamental theories. The Dewey Decimal Classification (DDC) system is used as a case study to test applicability of the FRSAD model for classification data, and as a springboard for a general discussion of issues related to the use of FRSAD for the representation of classification data.
  5. Mitchell, J.S.; Rype, I.; Svanberg, M.: Mixed translation models for the Dewey Decimal Classification (DDC) System (2008) 0.01
    0.013439858 = product of:
      0.053759433 = sum of:
        0.053759433 = weight(_text_:data in 2246) [ClassicSimilarity], result of:
          0.053759433 = score(doc=2246,freq=6.0), product of:
            0.14807065 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.046827413 = queryNorm
            0.3630661 = fieldWeight in 2246, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.046875 = fieldNorm(doc=2246)
      0.25 = coord(1/4)
    
    Content
    This paper explores the feasibility of developing mixed translations of the Dewey Decimal Classification (DDC system in countries/language groups where English enjoys wide use in academic and social discourse. A mixed translation uses existing DDC data in the vernacular plus additional data from the English-language full edition of the DDC to form a single mixed edition. Two approaches to mixed translations using Norwegian/English and Swedish/English DDC data are described, along with the design of a pilot study to evaluate use of a mixed translation as a classifier's tool.
  6. Zumer, M.; Zeng, M.L.; Mitchell, J.S.: FRBRizing KOS relationships : applying the FRBR model to versions of the DDC (2012) 0.01
    0.013439858 = product of:
      0.053759433 = sum of:
        0.053759433 = weight(_text_:data in 846) [ClassicSimilarity], result of:
          0.053759433 = score(doc=846,freq=6.0), product of:
            0.14807065 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.046827413 = queryNorm
            0.3630661 = fieldWeight in 846, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.046875 = fieldNorm(doc=846)
      0.25 = coord(1/4)
    
    Abstract
    The paper presents the approach of using the Functional Requirements for Bibliographic Records (FRBR) model to investigate the complicated sets of relationships among different versions of a classification system for the purposes of specifying provenance of classification data and facilitating collaborative efforts for using and reusing classification data, particularly in a linked data setting. The long-term goal of this research goes beyond the Dewey Decimal Classification that is used as a case. It addresses the questions of if and how the modelling approach and the FRBR-based model itself can be generalized and applied to other classification systems, multilingual and multicultural vocabularies, and even non-KOS resources that share similar characteristics.
  7. Beall, J.; Mitchell, J.S.: History of the representation of the DDC in the MARC Classification Format (2010) 0.01
    0.011199882 = product of:
      0.04479953 = sum of:
        0.04479953 = weight(_text_:data in 3568) [ClassicSimilarity], result of:
          0.04479953 = score(doc=3568,freq=6.0), product of:
            0.14807065 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.046827413 = queryNorm
            0.30255508 = fieldWeight in 3568, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3568)
      0.25 = coord(1/4)
    
    Abstract
    This article explores the history of the representation of the Dewey Decimal Classification (DDC) in the Machine Readable Cataloging (MARC) formats, with a special emphasis on the development of the MARC classification format. Until 2009, the format used to represent the DDC has been a proprietary one that predated the development of the MARC classification format. The need to replace the current editorial support system, the desire to deliver DDC data in a variety of formats to support different uses, and the increasingly global context of editorial work with translation partners around the world prompted the Dewey editorial team, along with OCLC research and development colleagues, to rethink the underlying representation of the DDC and choose the MARC 21 formats for classification and authority data. The discussion is framed with quotes from the writings of Nancy J. Williamson, whose analysis of the content of the Library of Congress Classification (LCC) schedules played a key role in shaping the original MARC classification format.
    Object
    MARC for Classification Data
  8. Mitchell, J.S.: DDC 22: Dewey in the world, the world in Dewey (2004) 0.01
    0.008866664 = product of:
      0.035466656 = sum of:
        0.035466656 = product of:
          0.07093331 = sum of:
            0.07093331 = weight(_text_:22 in 2644) [ClassicSimilarity], result of:
              0.07093331 = score(doc=2644,freq=10.0), product of:
                0.16398162 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046827413 = queryNorm
                0.43256867 = fieldWeight in 2644, product of:
                  3.1622777 = tf(freq=10.0), with freq of:
                    10.0 = termFreq=10.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2644)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Abstract
    In 2003, OCLC published Dewey Decimal Classification and Relative Index, Edition 22 (DDC 22), in print and Web versions. The changes and updates in the new edition reflect a modern view of knowledge structures and address the general needs of Dewey users. The content of DDC 22 has been shaped by a number of social, geopolitical, and technical trends. The World Wide Web has provided a vehicle for more frequent distribution of updates to the DDC, and a medium for direct communication with Dewey users around the world. In addition to updating the system itself, other strategies are needed to accommodate the needs of the global Dewey user community. Translation of the system is one approach; another is mapping. Mapping terminology to the DDC is a strategy for supporting effective local implementation of the system while maintaining the internal cohesiveness of the DDC. This paper explores the usefulness of mapping terminology from English-language general subject headings lists produced outside the U.S.
    Object
    DDC-22