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Peer Matrix Alignment: A New Algorithm

  • Mohammed Kayed
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7302)

Abstract

Web data extraction has been one of the keys for web content mining that tries to understand Web pages and discover valuable information from them. Most of the developed Web data extraction systems have used data (string/tree) alignment techniques. In this paper, we suggest a new algorithm for multiple string (peer matrix) alignment. Each row in the matrix represents one string of characters, where every character (symbol) corresponds to a subtree in the DOM tree of a web page. Two subtrees take the same symbol in the peer matrix if they are similar, where similarity can be measured using either structural, content, or visual information. Our algorithm is not a generalization of 2-strings alignment; it looks at multiple strings at the same time. Also, our algorithm considers the common problems in the field of Web data extraction: missing, multi-valued, multi-ordering, and disjunctive attributes. The experiments show a perfect alignment result with the matrices constructed from the nodes closed to the top (root) and an encourage result for the nodes closed to the leaves of the DOM trees of the test web pages.

Keywords

Text Alignment Tree Alignment Web Data Extraction Information Extraction 

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References

  1. 1.
    Crescenzi, V., Mecca, G., Merialdo, P.: Knowledge and Data Engineerings. In: Proc. Int’l Conf. Very Large Databases (VLDB), pp. 109–118 (2001)Google Scholar
  2. 2.
    Arasu, A., Garcia-Molina, H.: Extracting Structured Data from Web Pages. In: Proc. ACM SIGMOD, pp. 337–348 (2003)Google Scholar
  3. 3.
    Kayed, M., Chang, C.-H.: Page-level web data extraction from template pages. IEEE Trans. Know. and Data Eng. 22(2), 249–263 (2010)CrossRefGoogle Scholar
  4. 4.
    Chang, C.-H., Lui, S.-C.: IEPAD: Information Extraction Based on Pattern Discovery. In: Proc. Int’l Conf. World Wide Web (WWW-10), pp. 223–231 (2001)Google Scholar
  5. 5.
    Wang, J., Lochovsky, F.H.: Data Extraction and Label Assignment for Web Databases. In: Proc. Int’l Conf. World Wide Web (WWW-12), pp. 187–196 (2003)Google Scholar
  6. 6.
    Zhai, Y., Liu, B.: Web Data Extraction Based on Partial Tree Alignment. In: Proc. Int’l Conf. World Wide Web (WWW-14), pp. 76–85 (2005)Google Scholar
  7. 7.
    Chang, C.-H., Kayed, M., Girgis, M., Shaalan, K.: Survey of Web Information Extraction Systems. IEEE Trans. Know. and Data Eng. 18(10), 1411–1428 (2006)CrossRefGoogle Scholar
  8. 8.
    Yamada, Y., Craswell, N., Nakatoh, T., Hirokawa, S.: Testbed for Information Extraction from Deep Web. In: Proc. WWW-13, pp. 346–347 (2004)Google Scholar
  9. 9.
    Chang, C.-H., Kuo, S.-C.: OLERA: A semi-supervised approach for Web data extraction with visual support. IEEE Intelligent Systems 19(6), 56–64 (2004)CrossRefGoogle Scholar
  10. 10.
    Simon, K., Lausen, G.: ViPER: Augmenting Automatic Information Extraction with Visual Perceptions. In: Proc. CIKM (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mohammed Kayed
    • 1
  1. 1.Faculty of ScienceBeni-Suef UniversityEgypt

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