DocumentCode :
2112764
Title :
Matching Relevance Features with Ontological Concepts
Author :
Yan Shen ; Yuefeng Li ; Yue Xu ; Xiaohui Tao
Author_Institution :
Sci. & Eng. Fac., Queensland Univ. of Technol., Brisbane, QLD, Australia
Volume :
3
fYear :
2012
fDate :
4-7 Dec. 2012
Firstpage :
190
Lastpage :
194
Abstract :
In order to comprehend user information needs by concepts, this paper introduces a novel method to match relevance features with ontological concepts. The method first discovers relevance features from user local instances. Then, a concept matching approach is developed for matching these features to accurate concepts in a global knowledge base. This approach is significant for the transition of informative descriptor and conceptional descriptor. The proposed method is elaborately evaluated by comparing against three information gathering baseline models. The experimental results shows the matching approach is successful and achieves a series of remarkable improvements on search effectiveness.
Keywords :
feature extraction; information needs; information retrieval; knowledge based systems; ontologies (artificial intelligence); pattern matching; concept matching approach; conceptional descriptor; global knowledge base; informative descriptor; ontological concepts; relevance feature discovery; relevance feature matching; user information needs; user local instances; Concept Matching; Global Knowledge Base; Local Instance; Relevance Feature;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Web Intelligence and Intelligent Agent Technology (WI-IAT), 2012 IEEE/WIC/ACM International Conferences on
Conference_Location :
Macau
Print_ISBN :
978-1-4673-6057-9
Type :
conf
DOI :
10.1109/WI-IAT.2012.194
Filename :
6511675
Link To Document :
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