DocumentCode :
3312114
Title :
Improving Retrieval Performance with Wikipedia´s Category Knowledge
Author :
Zeng, Yin ; Lin, Wu ; Lei, Kai ; Huang, Lian´en
Author_Institution :
Shenzhen Key Lab. for Cloud Comput. Technol. & Applic. (SPCCTA), Peking Univ., Shenzhen, China
fYear :
2012
fDate :
17-19 Aug. 2012
Firstpage :
449
Lastpage :
452
Abstract :
For text search systems, the ambiguity of short queries often leads to poor performance. To solve this problem, relevance feedback via query-expansion is considered as one effective technique. However, many methods of relevance feedback barely use the knowledge of search results and the improvement of effectiveness is limited because the knowledge used is limited. In this paper we try to include Wikipedia´s category knowledge to improve the poor retrieval performance. A method of category feedback is proposed, which is based on the information of Wikipedia categories. Categories instead of terms and documents are provided to users for feedback. Finally, an experimental search system is developed which demonstrates the effectiveness of our method.
Keywords :
Web sites; query processing; relevance feedback; text analysis; Wikipedia category knowledge; category feedback; document handling; information retrieval; query expansion; query retrieval; relevance feedback; text search systems; Database languages; Electronic publishing; Encyclopedias; Information retrieval; Internet; Standards; Wikipedia; category feedback; information retrieval; query expansion;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational and Information Sciences (ICCIS), 2012 Fourth International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4673-2406-9
Type :
conf
DOI :
10.1109/ICCIS.2012.174
Filename :
6299999
Link To Document :
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