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folksonomy

Modeling a folksonomy with the postulational approach to facet analysis

An in-depth study of faceted classification theory, as presented by Ranganathan and further developed by Brian Vickery and the Classification Research Group, led to the creation of a methodology based on the Postulate of Fundamental Categories, the Postulate of Basic Facet and the Postulate of Isolate Facet. This methodology was then used to analyze the facets of a dataset consisting of over 107,000 instances of 1,275 unique tags representing 76 popular non-fiction history books collected from the LibraryThing folksonomy (see http://www.librarything.com/). Preliminary results of the facet analysis show the manually-produced, two-faceted classification models in the dataset, one representing the universe of books, and the other representing the universe of subjects within the universe of books. The model representing the universe of books is considered to be complete, whereas the model representing the universe of subjects is incomplete. These differences are discussed in the light of theoretical differences between special and universal faceted classifications. The model representing the universe of books is then compared to other models of books, including the BIBO ontology and the FRBR model. Finally, the models are discussed in terms of their confirmation or violation of Ranganathan’s seven Canons of Classification, upon which the postulates are based.

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English
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'And the winner is..' The perils and pitfalls of rank order analysis

Folksonomy research has not developed a standardized toolbox of analytical strategies, generally relying on descriptive methods to investigate the structure, composition and evolution of tagging vocabularies. Rank order (RO) based on tag frequency has been used to study various aspects of folksonomies: how well tags categorize resources (Brooks & Montanez, 2005; Kipp & Campbell, 2006) and identification of tagging patterns (Munk & Mork, 2007), and trends in user interest (Ding et al., 2009). However, results of simple RO may be misleading. For example, a tag whose frequency of use increases across two years may actually account for a smaller percentage of total tags assigned in the second year, or a tag whose rank declines across two years may actually account for a greater percentage of tags in the second year than the first. This research addresses the validity of assumptions underlying RO analysis by investigating how it correlates with both frequency and percentage of tag frequency across various time periods. Effects of RO by frequency of tag occurrence, taggers and URLs were investigated in a targeted sample collected from delicious.com for 2004 through 2007. After removing all singleton tags, the dataset consisted of 7,863 URLs to which 1,804,379 tags had been assigned by 186,075 taggers. Because of the non-normal distribution of tag frequency data, non-parametric statistical tests were used to calculate correlations. Results of the analysis found that the correlation between RO and frequency was high, but that correlation between RO and percentage of actual use was problematic.

This paper was not presented at the Conference due to illness of the authors.

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Talk
Language: 
English
An evaluation of enhancing social tagging with a knowledge organization system

Traditional subject indexing and classification are considered infeasible in many digital collections. Automated means and social tagging are often suggested as the two possible solutions. Both, however, have disadvantages and, depending on the purpose of use or context, require additional manual input. This study investigates ways of enhancing social tagging via knowledge organization systems, with a view to improving the quality of tags for increased information discovery and retrieval performance. Benefits of using both social tags and controlled terms are also explored, including enriching knowledge organization systems with new concepts.

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Talk
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English
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