Improved Vision Based Algorithm for Deep Web Data Extraction

Authors

  • Rashmi Chaudhary Computer Science and Engineering, Gautam Buddha University(GBU), Noida, India
  • Dr. Arun Solanki Computer Science and Engineering, Gautam Buddha University(GBU), Noida, India

DOI:

https://doi.org/10.37591/jowet.v2i2.376

Abstract

Several systems and languages have been proposed for solving web-data management problems, but none of existing system addresses all the problems from a unified perspective. Most of the existing link analysis algorithms treat a web page as a single node in the web graph. However, in most cases, a web page contains multiple semantics and hence the web page might not be considered as the atomic node. New web content, structure analysis based on visual representation is proposed in this paper. The web page is partitioned into blocks using the vision-based page segmentation algorithm. Many web applications such as information retrieval, information extraction and automatic page adaptation can benefit from this proposed methodology. This research presents an automatic top-down, tag-tree independent approach to detect web content structure. The proposed system simulates a user who understands web layout structure based on his/her visual perception. Comparing to other existing techniques, the proposed approach is independent to underlying documentation, representation such as HTML and works well even when the HTML structure is far different from layout structure.

Keywords: Deep web, cluster, VIPS, DOM, data region extraction, data record extraction

 

Cite this Article
Rashmi Chaudhary, Dr. Arun Solanki. Improved Vision Based Algorithm for Deep Web Data Extraction. Journal of Web Engineering & Technology.2015; 2(2): 23–32p.

Author Biography

  • Rashmi Chaudhary, Computer Science and Engineering, Gautam Buddha University(GBU), Noida, India

    Computer Science

    student

References

Wang J, Lochovsky FH. Data Extraction and Label Assignment for Web Databases. Proc. international conference on World Wide Web (WWW-12). 2003: 187–196p.

Zhai Y, Liu B. Web Data Extraction Based on Partial Tree Alignment. Proc. international conference on World Wide Web (WWW-14). 2005: 76–85p.

Cai D, Yu S, Wen J, Ma W. Extracting Content Structure for Web Page Based on Visual Representation. Proc. Asia Pacific Web Conf. (APWeb). 2003. 406–417p.

Laender M et al. Web data extraction, Application and technique. Univ. of Messina, Dept. of Mathematics and Informatics, viale F. Stagno D’Alcontres 31, I-98166 Messina, Italy dLixto Software GmbH, Austria.

Arocena GO, Mendelzon AO. WebOQL: Restructuring Documents, Databases, and Webs. Proc. Int’l Conf. Data Eng. (ICDE). 1998: 24–33p.

Ashraf F, Ozyer T, Alhajj R. Employing Clustering Techniques for Automatic Information Extraction from HTML Documents. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews. 2008; 38(5): 660–673p.

Lavanya M, Dhanalakshmi M. Various approaches of vision-based deep web data extraction and applications. International Journal of computer and Information Science and Engineering. 2013; 1(7).

Myllymaki Jussi. Effective Web Data Extraction with Standard XML Technologies. IBM Almaden Research Center 650 Harry Road San Jose, CA 95120, USA. 2012.

Sasikala D, Selva Kumar G. Extraction of Deep Web Contents. International Journal of Modern Engineering Research (IJMER). 2012; 2(1): 528–533p.

Shridevi A, Swami, Pujashree Vidap. Web Data Extraction and Alignment Tools: A Survey. International Journal of Scientific Engineering and Technology. 2013; 2(6): 573–578p.

Chang CH, Kayed M, Girgis MR, Shaalan KF. A Survey of Web Information Extraction Systems. IEEE Trans. Knowledge and Data Eng. 2006; 18(10): 1411–1428p.

Downloads

Published

2015-12-29

Issue

Vol. 2 No. 2 (2015)

Section

Research Articles

License


Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.