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Information Extraction by XLM

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4693))

Abstract

XML(eXtensible Markup Language) is used as the description form of the documents and the data exchanged on the Web. Now, the usage of the XML is extending to not only the exchange of the XML document but also that of the XML database for classification and retrieval. This paper develops how to retrieve the related objective data from the tree structure in the XML document for the classification. In this paper, an ordered preserving relation is defined as the cue of the retrieval of the objective pattern. Then, the problem is to find the ordered preserving relations in the document. In the document, the ordered and the unordered subtree structure are important in the retrieval and classification. Then, the retrieval value is calculated in the tree structure. A method developed here was applied to the practical XML document.

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References

  1. Zaki, M.J.: Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications. IEEE Trans. on Knowledge and Data Engineering 17(8), 1021–1035 (2005)

    Article  Google Scholar 

  2. Zaki, M.J.: Efficiently Mining Frequent Embedded Unordered Trees. Fundamenta Informatica 65, 1–20 (2005)

    MathSciNet  MATH  Google Scholar 

  3. Zaki, M.J., Aggarwal, C.C.: XRules, An Effective Structural Classifier for XML Data. In: Proc. of the 2003 Int. Conf. Knowledge Discovery and Data Mining, pp. 316–325 (2003)

    Google Scholar 

  4. Asai, T., Arimura, T., Uno, T., Nakano, S.: Discovering Frequent Substructures in Large Unordered Trees. In: Proc. Sixth Int. Conf. Discovery Science, pp. 47–61 (October 2003)

    Google Scholar 

  5. Chi, Y., Yang, Y., Munz, R.R.: Indexing and Mining Free Trees. In: Proc. Third IEEE Int. Conf. Data Mining, pp. 509–512. IEEE Computer Society Press, Los Alamitos (2003)

    Chapter  Google Scholar 

  6. Bao, Y., Tsuchiya, E., Ishii, N., Du, X.: Classification by Instance-Based Learning Algorithm. In: Gallagher, M., Hogan, J.P., Maire, F. (eds.) IDEAL 2005. LNCS, vol. 3578, pp. 133–140. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  7. Yamada, T., Yamashita, K., Ishii, N., Iwata, K.: Text Classification by Combining Different Distance Functions with Weights. In: SNPD2006, pp. 85–90. IEEE Computer Soc. Publication, Los Alamitos (2006)

    Google Scholar 

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© 2007 Springer-Verlag Berlin Heidelberg

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Okada, M., Ishii, N., Kato, N. (2007). Information Extraction by XLM. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4693. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74827-4_131

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  • DOI: https://doi.org/10.1007/978-3-540-74827-4_131

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74826-7

  • Online ISBN: 978-3-540-74827-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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