The goal of this work is to support deductive reasoning over unstructured data called a folksonomy and mapping the information induced by the folksonomy to an ontology. Folksonomy can be described as an implicit structure that emerges from an unstructured collection of keywords.
The concepts that describe and analyze the framework, called Folksonomy Space, support knowledge extraction from a folksonomy are also introduced. The dimensions of Folksonomy Space are explored for amazon.com that will help to delineate salient features of tag sets, tags, taggers, and referenced objects. An analytic framework is presented and the terms and concepts that support exposing the latent information in the folksonomies.
This work proposes several analytic methods and also the development of software techniques to make the implicit structures explicit in a folksonomy. The information induced from the folksonomy can be used for a variety of purposes.
","abstract_html":"<p>The goal of this work is to support deductive reasoning over unstructured data called a folksonomy and mapping the information induced by the folksonomy to an ontology. Folksonomy can be described as an implicit structure that emerges from an unstructured collection of keywords. </p><p>The concepts that describe and analyze the framework, called Folksonomy Space, support knowledge extraction from a folksonomy are also introduced. The dimensions of Folksonomy Space are explored for amazon.com that will help to delineate salient features of tag sets, tags, taggers, and referenced objects. An analytic framework is presented and the terms and concepts that support exposing the latent information in the folksonomies.</p><p>This work proposes several analytic methods and also the development of software techniques to make the implicit structures explicit in a folksonomy. The information induced from the folksonomy can be used for a variety of purposes.</p>","abstract_has_math":false,"creators":["Kirmani, Shad"],"institution":null,"degree_name":"M.S.","degree_level":"Campus Access Thesis","degree_discipline":"Computer Science and Engineering","degree_department":null,"school":null,"contributors":["Michael N. Huhns"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-01-01T08:00:00Z","date_published":"2010-01-01T08:00:00Z","updated_at":"2026-07-24T04:36:56Z","subjects":["Computer Sciences","Electrical and Computer Engineering","Engineering","Physical Sciences and Mathematics","AI","Data mining","Folksonomy","Induced Ontology","Ontology"],"languages":[],"rights":["© 2010, Shad Kirmani"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarcommons.sc.edu/etd/354","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Michael N. Huhns"]},{"key":"dc:creator","label":"Author","values":["Kirmani, Shad"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science and Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Campus Access Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Sciences","Electrical and Computer Engineering","Engineering","Physical Sciences and Mathematics","AI","Data mining","Folksonomy","Induced Ontology","Ontology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["© 2010, Shad Kirmani"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarcommons.sc.edu/etd/354"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The goal of this work is to support deductive reasoning over unstructured data called a folksonomy and mapping the information induced by the folksonomy to an ontology. Folksonomy can be described as an implicit structure that emerges from an unstructured collection of keywords.
The concepts that describe and analyze the framework, called Folksonomy Space, support knowledge extraction from a folksonomy are also introduced. The dimensions of Folksonomy Space are explored for amazon.com that will help to delineate salient features of tag sets, tags, taggers, and referenced objects. An analytic framework is presented and the terms and concepts that support exposing the latent information in the folksonomies.
This work proposes several analytic methods and also the development of software techniques to make the implicit structures explicit in a folksonomy. The information induced from the folksonomy can be used for a variety of purposes.
"]},{"key":"dc:title","label":"Title","values":["The Derivation of Ontological Structures From Folksonomies"]}]}],"canonical_facts":{"dc:contributor":["Michael N. Huhns"],"dc:creator":["Kirmani, Shad"],"dc:description.abstract":["The goal of this work is to support deductive reasoning over unstructured data called a folksonomy and mapping the information induced by the folksonomy to an ontology. Folksonomy can be described as an implicit structure that emerges from an unstructured collection of keywords.
The concepts that describe and analyze the framework, called Folksonomy Space, support knowledge extraction from a folksonomy are also introduced. The dimensions of Folksonomy Space are explored for amazon.com that will help to delineate salient features of tag sets, tags, taggers, and referenced objects. An analytic framework is presented and the terms and concepts that support exposing the latent information in the folksonomies.
This work proposes several analytic methods and also the development of software techniques to make the implicit structures explicit in a folksonomy. The information induced from the folksonomy can be used for a variety of purposes.
"],"dc:identifier":["https://scholarcommons.sc.edu/etd/354"],"dc:rights":["© 2010, Shad Kirmani"],"dc:subject":["Computer Sciences","Electrical and Computer Engineering","Engineering","Physical Sciences and Mathematics","AI","Data mining","Folksonomy","Induced Ontology","Ontology"],"dc:title":["The Derivation of Ontological Structures From Folksonomies"],"thesis:degree_discipline":["Computer Science and Engineering"],"thesis:degree_level":["Campus Access Thesis"],"thesis:degree_name":["M.S."]},"updated_at":"2026-07-24T04:36:56Z"}