DocumentCode :
2080278
Title :
Cluster tree based hybrid semantic similarity measure for social tagging systems
Author :
Zhang, Changli ; Zhang, Jinjin ; Yan, Maode
Author_Institution :
Sch. of Inf. Eng., Chang´´an Univ., Xi´´an, China
Volume :
2
fYear :
2010
fDate :
10-12 Dec. 2010
Firstpage :
1113
Lastpage :
1116
Abstract :
As the social tagging systems becoming prevalent, it remains a critical question that how to make explicit the semantics for tags to fully facilitate Web2.0 applications. This paper establishes a cluster tree based semantic similarity measure for social tagging systems, combines it with traditional statistics based measures into a hybrid one, tailors the hybrid measure according to the effectiveness requirement of intelligent search application, and presents a case study using the empirical data retrieved from delicious website. Comparing to the traditional statistics based measures, our hybrid measure is capable of evaluating similarities between random tags even not co-occurred, can better reflect the structural influence of the network of tag co-occurrence, and is feasible for applications like intelligent search in user-centric Web2.0 environment.
Keywords :
information retrieval; social networking (online); trees (mathematics); Web2.0 applications; Website; cluster tree; hybrid semantic similarity measure; intelligent search application; social tagging systems; TV; Web2.0; cluster tree; folkosonomy; intelligent search; semantic similarity measure; small world; social tagging systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Progress in Informatics and Computing (PIC), 2010 IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-6788-4
Type :
conf
DOI :
10.1109/PIC.2010.5687995
Filename :
5687995
Link To Document :
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