Abstract
Most existing web page classification algorithms, including content-based, link-based, or query-log analysis methods, treat the pages as smallest units. However, web pages usually contain some noisy or biased information which could affect the performance of classification. In this paper, we propose a Block Propagation Categorization (BPC) algorithm which deep mines web structure and views blocks as basic semantic units. Moreover, with query log information, BPC propagates only suitable information (block) among web pages to emphasize their topics. We also optimize the BPC algorithm to significantly speed up the block propagation process, without losing any precision. Our experiments on ODP and MSN search engine log show that BPC achieves a great improvement over traditional approaches.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Beeferman, D., Berger, A.: Agglomerative clustering of a search engine query log. In: Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 407–415 (2000)
Chakrabati, S., Dom, B., Indyk, P.: Enhanced hypertext categorization using hyperlinks. In: Proceedings of the ACM SIGMOD International Conference of Management of Data, Seattle, Washington, June 1998, pp. 307–318 (1998)
Chakrabarti, S.: Mining the Web: Discovering Knowledge from Hypertext Data. Morgan Kaufmann Publishers, San Francisco (2002)
Chuang, S.L., Chien, L.F.: Enriching Web taxonomies through subject categorization of query terms from search engine logs. Decision Support System 35(1) (April 2003)
Cohn, D., Hofmann, T.: The missing link – a probabilistic model of document content and hypertext connectivity. In: Advances in Neural Information Processing Systems, vol. 13, pp. 430–436. MIT Press, Cambridge (2001)
Cortes, C., Vapnik, V.: Support Vector Networks. Machine Learning 20, 1–25 (1995)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13, 21–27 (1967)
Glover, E.J., Tsioutsiouliklis, K., Lawrence, S., Pennock, D.M., Flake, G.W.: Using Web structure for classifying and describing Web pages. In: Proceedings of WWW 2002, International Conference on the World Wide Web (2002)
Lang, K.: Newsweeder: Learning to filter netnews. In: Proceedings of the 12th International Conference on Machine Learning, San Francisco, pp. 331–339 (1995)
Lewis, D.: Representation and learning in information retrieval. (COINS Technical Report 91-93). Dept. of Computer and Information Science, University of Massachusetts (1991)
Joachims, T.: A probabilistic analysis of the Rocchio algorithm with IFIDF for text categorization. Computer Science Technical Report CMU-CS-96-118. Carnegie Mellon University
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Panteleeva, N.: Using neighborhood information for automated categorization of Web, http://meta.math.spbu.ru/~nadejda/papers/ista2003/ista2003.html
Salton, G.: The SMART Retrieval System – Experiments in Automatic Document rocessing. Prentice Hall Inc., Englewood Cliffs (1971)
Salton, G., Lesk, M.E.: Computer evaluation of indexing and text processing. Journal of the ACM 15(1), 8–36 (1968)
Slattery, S., Craven, M.: Discovery test set regularities in relational domains. In: Proceedings of ICML 2000, 17th International Conference on Machine Learning, Stanford, US, pp. 895–902 (2000)
Xue, G.R., Shen, D., Yang, Q., Zeng, H.J., Chen, Z., Yu, Y., Ma, W.Y.: IRC: An Iterative Reinforcement Categorization Algorithm for Interrelated Web Objects. In: Proceedings of the 2004 IEEE International Conference on Data Mining (ICDM 2004), Brighton, United Kingdom (November 2004)
Wang, J.D., Zeng, H.J., Chen, Z., Lu, H.J., Tao, L., Ma, W.Y.: ReCoM: reinforcement clustering of multi-type interrelated data objects. In: Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, Toronto, CA, July 2003, pp. 274–281 (2003)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Proceeding of the Fourteenth International Conference of Machine Learning (1997)
Yang, Y.: An evaluation of statistical approaches to text categorization. Journal of Information Retrieval 1(1/2), 67–88 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dai, W., Yu, Y., Zhang, CL., Han, J., Xue, GR. (2006). A Novel Web Page Categorization Algorithm Based on Block Propagation Using Query-Log Information. In: Yu, J.X., Kitsuregawa, M., Leong, H.V. (eds) Advances in Web-Age Information Management. WAIM 2006. Lecture Notes in Computer Science, vol 4016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11775300_37
Download citation
DOI: https://doi.org/10.1007/11775300_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35225-9
Online ISBN: 978-3-540-35226-6
eBook Packages: Computer ScienceComputer Science (R0)