DocumentCode :
1478610
Title :
Clustering Method for Social Network Annotations
Author :
Astrain, J.J. ; Echarte, F. ; Córdoba, A. ; Villadangos, J.
Author_Institution :
Dept. de Ing. Mat. e Inf., Univ. Publica de Navarra, Pamplona, Spain
Volume :
8
Issue :
1
fYear :
2010
fDate :
3/1/2010 12:00:00 AM
Firstpage :
88
Lastpage :
93
Abstract :
Folksonomies are a widely used tool of collaboratively creating and managing tags to annotate and categorize Internet resources (Web 2.0). The process of annotation and tag management by users of social networks is extremely easy and simple; however, it involves serious problems of navigation and search unlike what happens with taxonomies, thesauri and ontologies. The use of fuzzy similarity measures allows the correct identification of syntactic variations when tag lengths are greater or equal than five symbols, been inadequate for smaller length tags. This article presents a method that combines both fuzzy similarity and cosine measures in order to provide a proper classification of tags even with smaller tag lengths. This method allows the proper classification of the 95% of the syntactic variations of tags analyzed in the experiments.
Keywords :
Internet; document handling; fuzzy set theory; pattern clustering; social networking (online); Internet resources; Web 2.0; clustering method; cosine measure; fuzzy similarity measure; social network annotation; syntactic variation; tag management; Clustering methods; Collaborative tools; Internet; Length measurement; Navigation; Ontologies; Resource management; Social network services; Taxonomy; Thesauri; folksonomies; fuzzy similarity; social network; social tagging; tag annotation; tag clustering;
fLanguage :
English
Journal_Title :
Latin America Transactions, IEEE (Revista IEEE America Latina)
Publisher :
ieee
ISSN :
1548-0992
Type :
jour
DOI :
10.1109/TLA.2010.5453951
Filename :
5453951
Link To Document :
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