Abstract
Measuring structural similarity between XML documents has become a key component in various applications, including XML data mining, schema matching, web service discovery, among others. The paper presents a novel structural similarity measure between XML documents using kernel methods. Results on preliminary simulations show that this outperforms conventional ones.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
Nierman, A., Jagadish, H.: Evaluating structural similarity in XML documents. In: Proceedings of the 5th International Workshop on the Web and Database (WebDB2002) (2002)
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)
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)
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)
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)
Kobayashi, M., Aono, M.: Vector Space Models for Search and Cluster Mining, pp. 103–122. Springer, New York (2003)
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)
Vert, J., Tsuda, K., Schölkopf, B.: A Primer on Kernel Methods, pp. 35–70. MIT Press, Cambridge (2004)
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)
Cancedda, N., Gaussier, E., Goutte, C., Renders, J.: Word-sequence kernels. Journal of Machine Learning Research 3, 1059–1082 (2003)
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)
Willett, P.: The porter stemming algorithm: Then and now. Electronic Library and Information Systems 40(3), 219–223 (2006)
Zhang, Z., Li, R., Cao, S., Zhu, Y.: Similarity metric for XML documents. In: Proceedings of Workshop on Knowledge and Experience Management (FGWM2003) (2003)
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)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Jeong, B., Lee, D., Cho, H., Kulvatunyou, B. (2007). A Kernel Method for Measuring Structural Similarity Between XML Documents. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_57
Download citation
DOI: https://doi.org/10.1007/978-3-540-73325-6_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73322-5
Online ISBN: 978-3-540-73325-6
eBook Packages: Computer ScienceComputer Science (R0)