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Information Retrieval from Deep Web Based on Visual Query Interpretation

Author

Listed:
  • Radhouane Boughammoura

    (Department of Sciences of Data Processing, Faculty of Sciences of Monastir, Research Unit MARS, Monastir, Tunisia)

  • Mohamed Nazih Omri

    (Department of Sciences of Data Processing, Faculty of Sciences of Monastir, Research Unit MARS, Monastir, Tunisia)

  • Lobna Hlaoua

    (Department of Electronic and Computer Science, High School of Sciences and Technology of H. Sousse, Research Unit MARS, H. Sousse, Tunisia)

Abstract

Deep Web is growing rapidly. More than 90% of relevant information in web comes from deep Web. Users are usually interested by products which satisfy their needs at the best prices and quality of service .Hence, user’s needs concerns not only one service but many competitive services at the same time. However, for commercial reasons, there is no way to compare all web services products. Each web service is a black box which accepts queries through its own query interface and returns results. As consequence, users ask separately different web services and spend a lot of time comparing products in order to find the best one. This is a burden for novice users. In this paper, the authors propose a new approach which integrates query interfaces of many web services into one universal web service. The new interface describes visually the universal query and is used to ask many web services at the same time. The authors have evaluated their approach on standard datasets and have proved good performances.

Suggested Citation

  • Radhouane Boughammoura & Mohamed Nazih Omri & Lobna Hlaoua, 2012. "Information Retrieval from Deep Web Based on Visual Query Interpretation," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 2(4), pages 45-59, October.
    • Handle: RePEc:igg:jirr00:v:2:y:2012:i:4:p:45-59
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