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

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Presentation on theme: "Towards a Norwegian general thesaurus? Unni Knutsen, Humanities and Social Sciences Library Mari Lundevall, Science Library."— Presentation transcript:

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

2 Subject indexing and classification ReportReport 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 1.A thesaurus (humanities and social sciences) –26 000 concepts 2.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:Report –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: 1.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 2.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 source vocabulary Term in target vocabulary μmapperExample

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