Query classification based on index association rule expansion

Authors:
Xianghua Fu;Dongjian Chen;Xueping Guo;Chao Wang
Affiliations:
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China;College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China;College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China;College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China
Venue:
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II
Year:
2011

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Abstract

Query classification can improve the query results of search engine, but the existing query classification methods which use extra web resources to enrich query features easily result in high delay. In this paper, a query classification based on index association rule expansion (IARE-QC) is proposed. IARE-QC uses an index based query classification framework to reduce the response time through transforming the query classification problem in online phase to the equivalent index term classification in offline phase. Moreover, in order to get more accurate feature enrichment of index term, we propose a novel algorithm which called index association expansion based on similarity voting (IARE-SV) to determine the category labels of index term. The experiment results on the search engine simulation environment show that IARE-SV can get much better query classification performance than the common simple voting (SV) method.