A Kernel Method for Measuring Structural Similarity Between XML Documents

1. Flesca, S., Manco, G., Masciari, E., Pontieri, L., Pugliese, A.: Fast detection of XML structural similarity. IEEE Transactions on Knowledge and Data Engineering 17(2) (February 2005)

   Google Scholar 

2. Yang, J., Cheung, W., Chen, X.: Learning the kernel matrix for XML document clustering. In: Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE’05), Washington, DC, pp. 353–358. IEEE Computer Society Press, Los Alamitos (2005)

   Chapter  Google Scholar 

3. Lee, J., Lee, K., Kim, W.: Preparations for semantics-based XML mining. In: Proceedings of IEEE International Conference on Data Mining (ICDM 2001), pp. 345–352 (2001)

   Google Scholar 

4. Nierman, A., Jagadish, H.: Evaluating structural similarity in XML documents. In: Proceedings of the 5th International Workshop on the Web and Database (WebDB2002) (2002)

   Google Scholar 

5. Shvaiko, P., Euzenat, J.: A survey of scham-based matching. In: Spaccapietra, S. (ed.) Journal on Data Semantics IV. LNCS, vol. 3730, pp. 14–171. Springer, Heidelberg (2005)

   Chapter  Google Scholar 

6. Jeong, B., Kulvatunyou, B., Ivezic, N., Cho, H., Jones, A.: Enhance reuse of standard e-business XML schema documents. In: Proceedings of International Workshop on Contexts and Ontology: Theory, Practice and Application (C&O’05) in the 20th National Conference on Artificial Intelligence (AAAI’05) (2005)

   Google Scholar 

7. Ivezic, N., Kulvatunyou, B., Frechette, S., Jones, A., Cho, H., Jeong, B.: An interoperability testing study: Automotive inventory visibility and interoperability. In: Proceedings of e-Challenges (2004)

   Google Scholar 

8. Muller, K., Mika, S., Ratsch, G., Tsuda, K., Schölkopf, B.: An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 12(2), 181–201 (2001)

   Article  Google Scholar 

9. Kobayashi, M., Aono, M.: Vector Space Models for Search and Cluster Mining, pp. 103–122. Springer, New York (2003)

   Google Scholar 

10. Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C.: Text classification using string kernels. Journal of Machine Learning Research 2, 419–444 (2002)

    Article  MATH  Google Scholar 

11. Vert, J., Tsuda, K., Schölkopf, B.: A Primer on Kernel Methods, pp. 35–70. MIT Press, Cambridge (2004)

    Google Scholar 

12. Saunders, C., Tschach, H., Shawe-Taylor, J.: Syllables and other string kernel extensions. In: Proceedings of the 19th International Conference on Machine Learning (ICML’02) (2002)

    Google Scholar 

13. Cancedda, N., Gaussier, E., Goutte, C., Renders, J.: Word-sequence kernels. Journal of Machine Learning Research 3, 1059–1082 (2003)

    Article  MATH  Google Scholar 

14. Jeong, B.: Machine Learning-based Semantic Similarity Measures to Assist Discovery and Reuse of Data Exchange XML Schemas. PhD thesis, Department of Industrial and Management Engineering, Pohang University of Science and Technology (2006)

    Google Scholar 

15. Willett, P.: The porter stemming algorithm: Then and now. Electronic Library and Information Systems 40(3), 219–223 (2006)

    Article  Google Scholar 

16. Zhang, Z., Li, R., Cao, S., Zhu, Y.: Similarity metric for XML documents. In: Proceedings of Workshop on Knowledge and Experience Management (FGWM2003) (2003)

    Google Scholar 

17. Reynolds, A., Richards, G., Rayward-Smith, V.: The application of k-medoids and PAM to the clustering of rules. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 173–178. Springer, Heidelberg (2004)

    Google Scholar 

Download references