lnu.sePublications
Change search
Refine search result
1 - 7 of 7
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Golub, Koraljka
    University of Bath, UK.
    Automated subject classification of textual documents in the context of Web-based hierarchical browsing2011In: Knowledge organization, ISSN 0943-7444, Vol. 38, no 3, p. 230-244Article in journal (Refereed)
    Abstract [en]

    While automated methods for information organization have been around for several decades now, exponential growth of the World Wide Web has put them into the forefront of research in different communities, within which several approaches can be identified: 1) machine learning (algorithms that allow computers to improve their performance based on learning from pre-existing data); 2) document clustering (algorithms for unsupervised document organization and automated topic extraction); and 3) string matching (algorithms that match given strings within larger text). Here the aim was to automatically organize textual documents into hierarchical structures for subject browsing. The string-matching approach was tested using a controlled vocabulary (containing pre-selected and pre-defined authorized terms, each corresponding to only one concept). The results imply that an appropriate controlled vocabulary, with a sufficient number of entry terms designating classes, could in itself be a solution for automated classification. Then, if the same controlled vocabulary had an appropriate hierarchical structure, it would at the same time provide a good browsing structure for the collection of automatically classified documents.

  • 2.
    Golub, Koraljka
    Linnaeus University, Faculty of Arts and Humanities, Department of Cultural Sciences.
    Automatic Subject Indexing of Text2019In: Knowledge organization, ISSN 0943-7444, Vol. 46, no 2, p. 104-121Article, review/survey (Refereed)
    Abstract [en]

    Automatic subject indexing addresses problems of scale and sustainability and can be at the same time used to enrich existing metadata records, establish more connections across and between resources from various metadata and resource collections, and enhance consistency of the metadata. In this work, automatic subject indexing focuses on assigning index terms or classes from established knowledge organization systems (KOSs) for subject indexing like thesauri, subject headings systems and classification systems. The following major approaches are discussed, in terms of their similarities and differences, advantages and disadvantages for automatic assigned indexing from KOSs: "text categorization," "document clustering," and "document classification." Text categorization is perhaps the most widespread, machine-learning approach with what seems generally good reported performance. Document clustering automatically both creates groups of related documents and extracts names of subjects depicting the group at hand. Document classification re-uses the intellectual effort invested into creating a KOS for subject indexing and even simple string-matching algorithms have been reported to achieve good results, because one concept can be described using a number of different terms, including equivalent, related, narrower and broader terms. Finally, applicability of automatic subject indexing to operative information systems and challenges of evaluation are outlined, suggesting the need for more research.

  • 3.
    Golub, Koraljka
    Linnaeus University, Faculty of Arts and Humanities, Department of Cultural Sciences.
    Subject Access in Swedish Discovery Services2018In: Knowledge organization, ISSN 0943-7444, Vol. 45, no 4, p. 297-309Article in journal (Refereed)
    Abstract [en]

    While support for subject searching has been traditionally advocated for in library catalogs, often in the form of a catalog objective to find everything that a library has on a certain topic, research has shown that subject access has not been satisfactory. Many existing online catalogs and discovery services do not seem to make good use of the intellectual effort invested into assigning controlled subject index terms and classes. For example, few support hierarchical browsing of classification schemes and other controlled vocabularies with hierarchical structures, few provide end-user-friendly options to choose a more specific concept to increase precision, a broader concept or related concepts to increase recall, to disambiguate homonyms, or to find which term is best used to name a concept. Optimum subject access in library catalogs and discovery services is analyzed from the perspective of earlier research as well as contemporary conceptual models and cataloguing codes. Eighteen proposed features of what this should entail in practice are drawn. In an exploratory qualitative study, the three most common discovery services used in Swedish academic libraries are analyzed against these features. In line with previous research, subject access in contemporary interfaces is demonstrated to less than optimal. This is in spite of the fact that individual collections have been indexed with controlled vocabularies and a significant number of controlled vocabularies have been mapped to each other and are available in interoperable standards. Strategic action is proposed to build research-informed (inter)national standards and guidelines.

  • 4. Golub, Koraljka
    et al.
    Hamon, Thierry
    Ardö, Anders
    Automated classification of textual documents based on a controlled vocabulary in engineering2007In: Knowledge organization, ISSN 0943-7444, Vol. 34, no 4, p. 247-263Article in journal (Refereed)
    Abstract [en]

    Automated subject classification has been a challenging research issue for many years now, receiving particular attention in the past decade due to rapid increase of digital documents. The most frequent approach to automated classification is machine learning. It, however, requires training documents and performs well on new documents only if these are similar enough to the former. We explore a string-matching algorithm based on a controlled vocabulary, which does not require training documents--instead it reuses the intellectual work put into creating the controlled vocabulary. Terms from the Engineering Information thesaurus and classification scheme were matched against title and abstract of engineering papers from the Compendex database. Simple string-matching was enhanced by several methods such as term weighting schemes and cut-offs, exclusion of certain terms, and enrichment of the controlled vocabulary with automatically extracted terms. The best results are 76% recall when the controlled vocabulary is enriched with new terms, and 79% precision when certain terms are excluded. Precision of individual classes is up to 98%. These results are comparable to state-of-the-art machine-learning algorithms.

  • 5.
    Hansson, Joacim
    Linnaeus University, Faculty of Arts and Humanities, Department of Cultural Sciences.
    The materiality of knowledge organization: epistemology, metaphors and society2013In: Knowledge organization, ISSN 0943-7444, Vol. 40, no 6, p. 384-391Article in journal (Refereed)
    Abstract [en]

    This article discusses the relation between epistemology, social organization and knowledge organization. Three examples are used to show how this relation has proven to be historically stable: 1) the organization of knowledge in 18th century encyclopedias; 2) the problem of bias in the international introduction of DDC in early 20th century libraries in Scandinavia; and 3) the practice of social tagging and folksonomies in contemporary late capitalist society. By using the concept of 'materiality' and the theoretical contribution on the documentality of social objects by Maurizio Ferraris, an understanding of the character of the connection between epistemology and social order in knowledge organization systems is achieved.

  • 6. Hansson, Joacim
    Why public libraries in sweden did nor choose Dewey.1997In: Knowledge organization, ISSN 0943-7444, Vol. 24, no 3, p. 145-153Article in journal (Refereed)
  • 7. Johansson, Sandra
    et al.
    Golub, Koraljka
    Linnaeus University, Faculty of Arts and Humanities, Department of Cultural Sciences.
    LibraryThing for Libraries: How Tag Moderation and Size Limitations Affect Tag Clouds2019In: Knowledge organization, ISSN 0943-7444, Vol. 46, no 4, p. 245-259Article in journal (Refereed)
    Abstract [en]

    The aim of this study is to analyse differences between tags on LibraryThing’s web page and tag clouds in their “LibraryThing for Libraries” service, and assess if, and how, the LibraryThing tag moderation and limitations to the size of the tag cloud in the library catalogue affect the description of the information resource. An e-mail survey was conducted with personnel at LibraryThing, and the results were compared against tags for twenty different fiction books, collected from two different library catalogues with disparate tag cloud sizes, and LibraryThing’s web page. The data were analysed using a modified version of Golder and Huberman’s tag categories (2006). The results show that while LibraryThing claims to only remove the inherently personal tags, several other types of tags are found to have been discarded as well. Occasionally a certain type of tag is included in one book, and excluded in another. The comparison between the two tag cloud sizes suggests that the larger tag clouds provide a more pronounced picture regarding the contents of the book but at the cost of an increase in the number of tags with synonymous or redundant information.

1 - 7 of 7
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf