Context Sensitive Search String Composition Algorithm using User Intention to Handle Ambiguous Keywords

Abstract

Finding the required URL among the first few result pages of a search engine is still a challenging task. This may require number of reformulations of the search string thus adversely affecting user's search time. Query ambiguity and polysemy are major reasons for not obtaining relevant results in the top few result pages. Efficient query composition and data organization are necessary for getting effective results. Context of the information need and the user intent may improve the autocomplete feature of existing search engines. This research proposes a Funnel Mesh-5 algorithm (FM5) to construct a search string taking into account context of information need and user intention with three main steps 1) Predict user intention with user profiles and the past searches via weighted mesh structure 2) Resolve ambiguity and polysemy of search strings with context and user intention 3) Generate a personalized disambiguated search string by query expansion encompassing user intention and predicted query. Experimental results for the proposed approach and a comparison with direct use of search engine are presented. A comparison of FM5 algorithm with K Nearest Neighbor algorithm for user intention identification is also presented. The proposed system provides better precision for search results for ambiguous search strings with improved identification of the user intention. Results are presented for English language dataset as well as Marathi (an Indian language) dataset of ambiguous search strings. Keyword: Autocompletion Context Data mining Search User intention Copyright © 2017 Institute of Advanced Engineering and Science.All rights reserved. Corresponding Author:Uma Gajendragadkar, COEP, Phone +919822479128, G7/9 Omkar Garden, Manikbaug, Pune, Maharshtra, India. Email: umagadkar@gmail.com INTRODUCTIONCurrent search engines churn a large volume of data to obtain meaningful information; however, the main challenge is to get relevant results in the top few result pages [1], [2]. Search engines check for the presence of keywords in documents. Mere presence of keywords in a document may not match the user's search intention and need. User satisfaction increases when more relevant and exact information is presented in the top few results. An appropriately composed query is the starting point for handling this challenge [3]. Performance of search engines can be improved with the use of appropriate keywords or prediction of such keywords [4][5][6]. Search engines use search logs and most popular queries; however, these are not sufficient to predict the user's interests or intention [7].Users are of three types, first -Internet skilled users, second -Internet aware users and thirdInternet unskilled users. Many times, users do not know the proper keywords for searching information and they cannot express their information need or intent of search [8], [9]. This results in search results often not satisfying user's information need. This problem can be addressed by query expansion and reformulation [3]. Search engines provide a...

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