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A Visual Based Page Segmentation for Deep Web Data Extraction

  • Conference paper
Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 131))

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Abstract

A new web content structure analysis based on visual representation is proposed in this paper. Many web applications such as information retrieval, information extraction and automatic page adaptation can benefit from this structure. This paper presents an automatic top-down, tag-tree independent approach to detect web content structure. It simulates how a user understands web layout structure based on his visual perception. Comparing to other existing techniques such as DOM tree, our approach is independent to the HTML documentation representation. Our method can work well even when the HTML structure is quite different from the visual layout structure.

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References

  1. Adelberg, B.: NoDoSE: A tool for semi-automatically extracting structured and semi-structured data from text documents. In: Proceedings of ACM SIGMOD Conference on Management of Data, pp. 283–294 (1998)

    Article  Google Scholar 

  2. Ashish, N., Knoblock, C.A.: Semi-Automatic Wrapper Generation for Internet Information Sources. In: Proceedings of the Conference on Cooperative Information Systems, pp. 160–169 (1997)

    Google Scholar 

  3. Ashish, N., Knoblock, C.A.: Wrapper Generation for Semi-structured Internet Sources. SIGMOD Record 26(4), 8–15 (1997)

    Article  Google Scholar 

  4. Bailey, P., Craswell, N., Hawking, D.: Engineering a multi-purpose test collection for Web retrieval experiments. Information Processing and Management (2001)

    Google Scholar 

  5. Bar-Yossef, Z., Rajagopalan, S.: Template Detection via Data Mining and its Applications. In: Proceedings of the 11th International World Wide Web Conference, WWW 2002 (2002)

    Google Scholar 

  6. Bernard, M.L.: Criteria for optimal web design (designing for usability) (2002)

    Google Scholar 

  7. Bharat, K., Henzinger, M.R.: Improved algorithms for topic distillation in a hyperlinked environment. In: Proceedings of the 21st ACM International Conference on Research and Development in Information Retrieval (SIGIR 1998), pp. 104–111 (1998)

    Google Scholar 

  8. Buckley, C., Salton, G., Allan, J.: Automatic Retrieval with Locality Information Using Smart. In: The First Text REtrieval Conference (TREC-1), National Institute of Standards and Technology, Gaithersburg, MD, pp. 59–72 (1992)

    Google Scholar 

  9. Buttler, D., Liu, L., Pu, C.: A Fully Automated Object Extraction System for the World Wide Web. In: International Conference on Distributed Computing Systems (2001)

    Google Scholar 

  10. Buyukkokten, O., Garcia-Molina, H., Paepche, A.: Accordion Summarization for End-GameBrowsing on PDAs and Cellular Phones. In: Proceedings of the Conference on Human Factors in Computing Systems, CHI 2001 (2001)

    Google Scholar 

  11. Chakrabarti, S.: Integrating the Document Object Model with hyperlinks for enhanced topicdistillation and information extraction. In: In the 10th International World Wide Web Conference (2001)

    Google Scholar 

  12. Chakrabarti, S., Joshi, M., Tawde, V.: Enhanced topic distillation using text, markup tags, and hyperlinks. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 208–216. ACM Press (2001)

    Google Scholar 

  13. Chakrabarti, S., Punera, K., Subramanyam, M.: Accelerated focused crawling through online relevance feedback. In: Proceedings of the Eleventh International Conference on World Wide Web (WWW 2002), pp. 148–159 (2002)

    Google Scholar 

  14. Chen, J., Zhou, B., Shi, J., Zhang, H.-J., Wu, Q.: Function-Based Object Model Towards Website Adaptation. In: Proceedings of the 10th International World Wide Web Conference (2001)

    Google Scholar 

  15. Diao, Y., Lu, H., Chen, S., Tian, Z.: Toward Learning Based Web Query Processing. In: Proceedings of International Conference on Very Large Databases, pp. 317–328 (2000)

    Google Scholar 

  16. Hammer, J., Garcia-Molina, H., Cho, J., Aranha, R., Crespo, A.: Extracting Semi-structured Information from the Web. In: Proceedings of the Workshop on Management for Semi-structured Data, pp. 18–25 (1997)

    Google Scholar 

  17. Embley, D.W., Jiang, Y., Ng, Y.-K.: Record-boundary discovery in Web documents. In: Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, Philadelphia PA, pp. 467–478 (1999)

    Google Scholar 

  18. Kleinberg, J.: Authoritative sources in a hyperlinked environment. In: Proceedings of the 9th ACM-SIAM Symposium on Discrete Algorithms, Baltimore, MD, USA, pp. 668–677 (1998)

    Google Scholar 

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Authors and Affiliations

  1. M. E. I. T. Prof. Ram Meghe Institute of Technology & Research, Badnera, Maharashatra, India

    Vikas R. Palekar

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  1. Vikas R. Palekar
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Correspondence to Vikas R. Palekar .

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Editors and Affiliations

  1. , Department of Mathematics, Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, 247667, India

    Kusum Deep

  2. Department of Mathematics and, Computer Science, Liverpool Hope University, Hope Park, Liverpool, L16 9JD, United Kingdom

    Atulya Nagar

  3. , Department of Paper Technology, Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, 247667, India

    Millie Pant

  4. Information Technology and Management, ABV-Indian Institute of, 109, E- Block, Gwalior, 474010, India

    Jagdish Chand Bansal

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Palekar, V.R. (2012). A Visual Based Page Segmentation for Deep Web Data Extraction. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_72

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  • DOI: https://doi.org/10.1007/978-81-322-0491-6_72

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-0490-9

  • Online ISBN: 978-81-322-0491-6

  • eBook Packages: EngineeringEngineering (R0)

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