Web Form Entrance Detection and Automatic Form Filling

Wen Qian Shang

doi:10.4028/www.scientific.net/AMM.380-384.2712

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 380-384Web Form Entrance Detection and Automatic Form...

Web Form Entrance Detection and Automatic Form Filling

Abstract:

At present, deep web information mining is a considerably potential research area. How to get this massive and valuable information hidden after the database is need to be studied further. So this paper presents an approach that includes web pages analysis, getting forms, form analysis, automatic form filling, automatic form submission and acquiring returning pages. The aim is to let the computer automatically complete this process. The experimental results show the feasibility of this method. It can automatically complete the entire process.

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Periodical:

Applied Mechanics and Materials (Volumes 380-384)

Pages:

2712-2715

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.380-384.2712

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Online since:

August 2013

Authors:

Wen Qian Shang

Keywords:

Deep Web, Form Detection, Form Submission

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References

[1] Barbosa L, Freire J. Combining classifiers to identify online databases．Proc of the 16th International Conference on World Wide Web [C]. (2007).

DOI: 10.1145/1242572.1242631

Google Scholar

[2] James Al1an. Topic Detection and Tracking: Event-based Information Organization. K1uwer Academic Publishers，(2002).

Google Scholar

[3] Ntoulas A, Zerfos P, Cho J. Downloading Textual Hidden WebContent through Keyword Queries. In JCDL, (2005).

DOI: 10.1145/1065385.1065407

Google Scholar

[4] Baeza Y, Ribeiro N. Modern Information Retrieval. New York：ACM Press/Addison-Weslev, (1999).

Google Scholar

[5] Barbosa L, Freire J. Organizing Hidden-Web Databases by C1ustering Visible Web Documents. Proc of the IEEE 2 3rd International Conference oil Data Engineering[C]. (2007).

DOI: 10.1109/icde.2007.367878

Google Scholar

[6] Wang Y, Kitsuregawa M. Evaluating contents-link coupled web page clustering for web search results. In CIKM. (2002).

DOI: 10.1145/584792.584875

Google Scholar