Paper Info
Title
Mining Web search engines for query suggestion
Abstract
Queries to Web search engines are usually short and ambiguous, which provides insufficient information needs of users for effectively retrieving relevant Web pages. To address this problem, query suggestion is implemented by most search engines. However, existing methods do not leverage the contradiction between accuracy and computation complexity appropriately (e.g. Google's ‘Search related to’ and Yahoo's ‘Also Try’). In this paper, the recommended words are extracted from the search results of the query, which guarantees the real time of query suggestion properly. A scheme for ranking words based on semantic similarity presents a list of words as the query suggestion results, which ensures the accuracy of query suggestion. Moreover, the experimental results show that the proposed method significantly improves the quality of query suggestion over some popular Web search engines (e.g. Google and Yahoo). Finally, an offline experiment that compares the accuracy of snippets in capturing the number of words in a document is performed, which increases the confidence of the method proposed by the paper. Copyright © 2010 John Wiley & Sons, Ltd.
Year
DOI
Venue
2011
10.1002/cpe.1689
Concurrency and Computation: Practice and Experience
Keywords
DocType
Volume
Mining Web search engine,Web search engine,computation complexity,popular Web search engine,query suggestion result,search engine,John Wiley,relevant Web page,query suggestion,proposed method,search result
Journal
23
Issue
ISSN
Citations 
10
1532-0626
9
PageRank 
References 
Authors
0.74
10
4
Name
Order
Citations
PageRank
Zheng Xu135219.51
Xiangfeng Luo21251124.38
Jie Yu390.74
Weimin Xu4617.98