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Assisting Web Search Using Query Suggestion Based on Word Similarity Measure and Query Modification Patterns

By Rani Qumsiyeh and Yiu-kai Ng

Abstract

One of the useful tools offered by existing web search engines is query suggestion (QS), which assists users in formulating keyword queries by suggesting keywords that are unfamiliar to users, offering alternative queries that deviate from the original ones, and even correcting spelling errors. The design goal of QS is to enrich the web search experience of users and avoid the frustrating process of choosing controlled keywords to specify their special information needs, which releases their burden on creating web queries. Unfortunately, the algorithms or design methodologies of the QS module developed by Google, the most popular web search engine these days, is not made publicly available, which means that they cannot be duplicated by software developers to build the tool for specifically-design software systems for enterprise search, desktop search, or vertical search, to name a few. Keyword suggested by Yahoo! and Bing, another two well-known web search engines, however, are mostly popular currently-searched words, which might not meet the specific information needs of the users. These problems can be solved by WebQS, our proposed web QS approach, which provides the same mechanism offered by Google, Yahoo!, and Bing to support users in formulatin

Topics: either because they are in a hurry, use inappropriate keywords, or do not understand the search process well. These scenarios might explain why web s
Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.353.282
Provided by: CiteSeerX
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