Learning personalized tag ontology from user tagging information
Djuana, Endang, Xu, Yue, & Li, Yuefeng (2012) Learning personalized tag ontology from user tagging information. In Zhao, Yanchang, Li, Jiuyong, Kennedy, Paul, & Christen, Peter (Eds.) Conferences in Research and Practice in Information Technology (CRPIT), Australian Computer Society, Inc., Sydney, N. S. W.
The cross-sections of the Social Web and the Semantic Web has put folksonomy in the spot light for its potential in overcoming knowledge acquisition bottleneck and providing insight for "wisdom of the crowds". Folksonomy which comes as the results of collaborative tagging activities has provided insight into user's understanding about Web resources which might be useful for searching and organizing purposes. However, collaborative tagging vocabulary poses some challenges since tags are freely chosen by users and may exhibit synonymy and polysemy problem. In order to overcome these challenges and boost the potential of folksonomy as emergence semantics we propose to consolidate the diverse vocabulary into a consolidated entities and concepts. We propose to extract a tag ontology by ontology learning process to represent the semantics of a tagging community. This paper presents a novel approach to learn the ontology based on the widely used lexical database WordNet. We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users’ tagging behavior together. We provide empirical evaluations by using the semantic information contained in the ontology in a tag recommendation experiment. The results show that by using the semantic relationships on the ontology the accuracy of the tag recommender has been improved.
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|Item Type:||Conference Paper|
|Additional Information:||Copyright(c)2012, Australian Computer Society, Inc. This paper appeared at the 10th Australasian Data Mining Conference (AusDM 2012), Sydney, Australia, December 2012. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 134, Yanchang Zhao, Jiuyong Li, Paul Kennedy, and Peter Christen, Ed. Reproduction for academic, not-for profit purposes permitted provided this text is included.|
|Keywords:||collaborative tagging, folksonomy, ontology learning, personalization, tag recommendation|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Organisation of Information and Knowledge Resources (080707)
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2012 Australian Computer Society, Inc.|
|Deposited On:||02 Jan 2013 05:43|
|Last Modified:||06 Aug 2014 09:55|
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