Difference between revisions of "Construction of Disambiguated Folksonomy Ontologies Using Wikipedia"

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'''Construction of Disambiguated Folksonomy Ontologies Using Wikipedia''' - scientific work related to Wikipedia quality published in 2009, written by Noriko Tomuro and Andriy Shepitsen.
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'''Construction of Disambiguated Folksonomy Ontologies Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Noriko Tomuro]] and [[Andriy Shepitsen]].
  
 
== Overview ==
 
== Overview ==
One of the difficulties in using Folksonomies in computational systems is tag ambiguity: tags with multiple meanings. This paper presents a novel method for building Folksonomy tag ontologies in which the nodes are disambiguated. Authors method utilizes a clustering algorithm called DSCBC, which was originally developed in Natural Language Processing (NLP), to derive committees of tags, each of which corresponds to one meaning or domain. In this work, authors use Wikipedia as the external knowledge source for the domains of the tags. Using the committees, an ambiguous tag is identified as one which belongs to more than one committee. Then authors apply a hierarchical agglomerative clustering algorithm to build an ontology of tags. The nodes in the derived ontology are disambiguated in that an ambiguous tag appears in several nodes in the ontology, each of which corresponds to one meaning of the tag. Authors evaluate the derived ontology for its ontological density (how close similar tags are placed), and its usefulness in applications, in particular for a personalized tag retrieval task. The results showed marked improvements over other approaches.
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One of the difficulties in using Folksonomies in computational systems is tag ambiguity: tags with multiple meanings. This paper presents a novel method for building Folksonomy tag ontologies in which the nodes are disambiguated. Authors method utilizes a clustering algorithm called DSCBC, which was originally developed in [[Natural Language Processing]] (NLP), to derive committees of tags, each of which corresponds to one meaning or domain. In this work, authors use [[Wikipedia]] as the external knowledge source for the domains of the tags. Using the committees, an ambiguous tag is identified as one which belongs to more than one committee. Then authors apply a hierarchical agglomerative clustering algorithm to build an [[ontology]] of tags. The nodes in the derived ontology are disambiguated in that an ambiguous tag appears in several nodes in the ontology, each of which corresponds to one meaning of the tag. Authors evaluate the derived ontology for its ontological density (how close similar tags are placed), and its usefulness in applications, in particular for a personalized tag retrieval task. The results showed marked improvements over other approaches.

Revision as of 14:19, 22 December 2019

Construction of Disambiguated Folksonomy Ontologies Using Wikipedia - scientific work related to Wikipedia quality published in 2009, written by Noriko Tomuro and Andriy Shepitsen.

Overview

One of the difficulties in using Folksonomies in computational systems is tag ambiguity: tags with multiple meanings. This paper presents a novel method for building Folksonomy tag ontologies in which the nodes are disambiguated. Authors method utilizes a clustering algorithm called DSCBC, which was originally developed in Natural Language Processing (NLP), to derive committees of tags, each of which corresponds to one meaning or domain. In this work, authors use Wikipedia as the external knowledge source for the domains of the tags. Using the committees, an ambiguous tag is identified as one which belongs to more than one committee. Then authors apply a hierarchical agglomerative clustering algorithm to build an ontology of tags. The nodes in the derived ontology are disambiguated in that an ambiguous tag appears in several nodes in the ontology, each of which corresponds to one meaning of the tag. Authors evaluate the derived ontology for its ontological density (how close similar tags are placed), and its usefulness in applications, in particular for a personalized tag retrieval task. The results showed marked improvements over other approaches.