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Efficient Implementation of Web Search Query Reformulation Using Ant Colony Optimization

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Big Data Analytics (BDA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8883))

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Abstract

Typically, web search users submit short and ambiguous queries to search engines. As a result, users spent much time in formulating query in order to retrieve relevant information in the top ranked results. In this paper, term association graph is employed in order to provide query suggestion by assessing the linkage structure of the text graph constructed over a collection of documents. In addition to that, a biologically inspired model based on Ant Colony Optimisation (ACO) has been explored and applied over term association graph as learning process that addresses the problem of deriving optimal query suggestions. The user interactions with the search engine is treated as an individual ant’s navigation and the collective navigations of all ants over the time result in strengthening more significant paths in a term association graph which in turn used to provide query modification suggestions. We present an algorithm that attempts to select the best related keyword among all possible suggestions for an input search query and discuss its implementation based on a ternary search tree and graph data structure. We experimentally study the performance of the proposed method in comparing with different techniques.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Government College of Technology, Coimbatore, India

    K. Veningston & R. Shanmugalakshmi

Authors
  1. K. Veningston
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  2. R. Shanmugalakshmi
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Editor information

Editors and Affiliations

  1. International Institute of Information Technology - Bangalore, 26/C, Electronics City, Hosur Road, 560100, Bangalore, India

    Srinath Srinivasa

  2. IBM Research - India, 4 Block C, Institutional Area, Vasant Kunj, 110070, New Delhi, India

    Sameep Mehta

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Veningston, K., Shanmugalakshmi, R. (2014). Efficient Implementation of Web Search Query Reformulation Using Ant Colony Optimization. In: Srinivasa, S., Mehta, S. (eds) Big Data Analytics. BDA 2014. Lecture Notes in Computer Science, vol 8883. Springer, Cham. https://doi.org/10.1007/978-3-319-13820-6_7

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  • DOI: https://doi.org/10.1007/978-3-319-13820-6_7

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13819-0

  • Online ISBN: 978-3-319-13820-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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