Skip to main content
Presentation
A Probabilistic Query Suggestion Approach Without Using Query Logs
2013 IEEE 25th International Conference on Tools with Artificial Intelligence (ICTAI) (2013)
  • Meher T. Shaikh, Brigham Young University
  • Maria S. Pera, Brigham Young University
  • Yiu-Kai Ng, Brigham Young University
Abstract
Commercial web search engines include a query suggestion module so that given a user's keyword query, alternative suggestions are offered and served as a guide to assist the user in formulating queries which capture his/her intended information need in a quick and simple manner. Majority of these modules, however, perform an in-depth analysis of large query logs and thus (i) their suggestions are mostly based on queries frequently posted by users and (ii) their design methodologies cannot be applied to make suggestions on customized search applications for enterprises for which the irrespective query logs are not large enough or non-existent. To address these design issues, we have developed PQS, a probabilistic query suggestion module. Unlike its counterparts, PQS is not constrained by the existence of query logs, since it solely relies on the availability of user-generated content freely accessible online, such as the Wikipedia.org document collection, and applies simple, yet effective, probabilistic-and information retrieval-based models, i.e., the Multinomial, Bigram Language, and Vector Space Models, to provide useful and diverse query suggestions. Empirical studies conducted using a set of test queries and the feedbacks provided by Mechanical Turk appraisers have verified that PQS makes more useful suggestions than Yahoo! and is almost as good as Google and Bing based on the relatively small difference in performance measures achieved by Google and Bing over PQS.
Keywords
  • query suggestion,
  • classification,
  • probabilities
Publication Date
November 4, 2013
Citation Information
Meher T. Shaikh, Maria S. Pera and Yiu-Kai Ng. "A Probabilistic Query Suggestion Approach Without Using Query Logs" 2013 IEEE 25th International Conference on Tools with Artificial Intelligence (ICTAI) (2013)
Available at: http://works.bepress.com/maria_pera/1/