to_be_classified: A Facet Analysis of a Folksonomy
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This research examines Ranganathan’s postulational approach to facet analysis with the intention of manually inducing a faceted classification ontology from a folksonomy. Folksonomies are viewed as a source to a wealth of data representing users’ perspectives. An in-depth study of faceted classification theory is used to form a methodology based on the postulational approach. The dataset used to test the methodology consists of over 107,000 instances of 1,275 unique tags representing 76 popular non-fiction history books collected from the LibraryThing folksonomy. Preliminary results of the facet analysis indicate the manual inducement of two faceted classification ontologies in the dataset; one representing the universe of books and one representing the universe of subjects within the universe of books. The ontology representing the universe of books is considered to be complete, whereas the ontology representing the universe of subjects is incomplete. These differences are discussed in light of theoretical differences between special and universal faceted classifications. The induced ontologies are then discussed in terms of their substantiation or violation of Ranganathan’s Canons of Classification.
Master i bibliotek- og informasjonsvitenskap