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Subject indexing and classification

Towards a Norwegian general thesaurus? Unni Knutsen, Humanities and Social Sciences Library Mari Lundevall , Science Library. Subject indexing and classification. Report from working group (2010): Lack of coordination across (and within) faculties:

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Subject indexing and classification

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  1. Towards a Norwegian general thesaurus?Unni Knutsen, Humanities and Social Sciences LibraryMari Lundevall, Science Library

  2. Subject indexing and classification Report from working group (2010): • Lack of coordination across (and within) faculties: • Uncontrolled index terms, controlled index terms, two thesauri: Humord (humanities and social sciences) and MESH • 7 different classification schemes (including DDC and NLM Classification) • Resource constraints

  3. Recommendations • Use established subject indexing systems • Extend Humord to include most of the subject areas in the library • In addition: MESH • Do away with in-house classification schemes, use DDC and NLM Classification

  4. More about Humord • A thesaurus (humanities and social sciences) • 26 000 concepts • In addition the name of a joint indexing activity within the framework of the shared catalogue – BIBSYS • Cooperation and reuse of indexing data • Consistent use of indexing terms based on common indexing rules

  5. National Library of Norway • Report from working group: • Various in-house subject indexing systems • Lack of coordination • Lack of standardized subject indexing systems • Tested data from various sources. Conclusion: Use of Humord gave the best result

  6. So we joined forces… • The University of Oslo Library received funds for 2014 to: • Participate in a study with the National Library to explore the feasibility of developing a more general, national thesaurus based on Humord and the controlled vocabulary for natural sciences and mathematics • Develop methods of mapping from Humord to WebDewey

  7. Realfagstermer • Controlled vocabulary for natural sciences and mathematics • 14 000 concepts • Synonym control • Related terms • English (10%), some Latin • Subject strings

  8. Mapping attempts • Target: the on-going Norwegian DDC-translation • 500-group, 600-640 • Computer assisted direct matching: Mapping suggestions based on string matching from our terms to DDC captions • Co-occurrence mapping: Mapping suggestions based on co-existing DDC and subject terms in catalogue records

  9. Term in sourcevocabulary Term in target vocabulary μmapper Example

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