DocumentCode :
2253175
Title :
ECKDF: Extended conceptual knowledge discovery in folksonomy
Author :
Hao, Fei ; Zhong, Shengtong
Author_Institution :
Dept. of Comput. Sci., Korea Adv. Inst. of Sci. & Technol., Daejeon, South Korea
fYear :
2010
fDate :
3-5 Dec. 2010
Firstpage :
71
Lastpage :
76
Abstract :
Social bookmarking tools are rapidly emerging on the Web. A conceptual structure called folksonomy plays an important role in such systems. The folksonomy is constitute of tagging data(users, tags, resources) which organizing and classifying information on the Web. Tagging data stored in the folksonomy includes a lot of very useful information and knowledge. Unlike ontologies, shared conceptualizations in folksonomy are not formalized and it is rather implicit. The hidden knowledge Discovering from folksonomy is becoming the main research task among the social sharing resources systems. In this paper, we propose an approach of folksonomy data mining based on Variable Precise Concepts (VPC) for discovering the extended conceptual knowledge(tag recommendation, resources suggestion) from folksonomy. Finally, the feasibility and efficiency of our approach are demonstrated by experiments.
Keywords :
data mining; social networking (online); ECKDF; extended conceptual knowledge discovery; folksonomy data mining; hidden knowledge Discovering; information classifying; ontologies; shared conceptualizations; social bookmarking tools; social sharing resources systems; tagging; variable precise concepts; Artificial intelligence; Artificial neural networks; Context; Data mining; Delta modulation; Lattices; Tagging;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Problem-Solving (ICCP), 2010 International Conference on
Conference_Location :
Lijiang
Print_ISBN :
978-1-4244-8654-0
Type :
conf
Filename :
5696014
Link To Document :
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