Abstract
When we have a large amount of information, we usually use categories with a hierarchy, in which all information is assigned. The Yahoo! Internet directory is one such example. This paper proposes a new method of integrating two catalogs with hierarchical categories. The proposed method uses not only the contents of information but also the structures of both hierarchical categories. In order to evaluate the proposed method, we conducted experiments using two actual Internet directories, Yahoo! and Google. The results show improved performance compared with the previous approaches.
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.
Similar content being viewed by others
References
Agrawal, R., Srikant, R.: On integrating catalogs. In: Proc. of the Tenth Int. WWW Conf., pp. 603–612 (2001)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
dmoz (2003), http://dmoz.org/
Doan, A., Madhavan, J., Domingos, P., Halevy, A.: Learning to map between ontologies on the semantic web. In: Proc. of the 11th Int. WWW Conf. (2002)
Fleiss, J.: Statistical Methods for Rates and Proportions. John Wiley & Sons, Chichester (1973)
Google (2003), http://directory.google.com/
Ichise, R., Takeda, H., Honiden, S.: Integrating multiple internet directories by instance-based learning. In: Proc. of the 18th Int. Joint Conf. on AI, pp. 22-28 (2003)
Koller, D., Sahami, M.: Hierarchically classifying documents using very few words. In: Proc. of the 14th Int. Conf. on Machine Learning, pp. 170–178 (1997)
McCallum, A.K.: Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering mccallum/bow/ (1996), http://www.cs.cmu.edu/
McCallum, A.K., Rosenfeld, R., Mitchell, T.M., Ng, A.Y.: Improving text classification by shrinkage in a hierarchy of classes. In: Proc. of the 15th Int. Conf. on Machine Learning, pp. 359–367 (1998)
McGuinness, D.L., Fikes, R., Rice, J., Wilder, S.: An environment for merging and testing large ontologies. In: Proc. of the Conf. on Principles of Knowledge Representation and Reasoning, pp. 483–493 (2000)
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Noy, N.F., Musen, M.A.: Prompt: Algorithm and tool for automated ontology merging and alignment. In: Proc. of the 17th National Conf. on AI, pp. 450–455 (2000)
Omelayenko, B., Fensel, D.: An analysis of B2B catalogue integration problems. In: Proc. of the Int. Conf. on Enterprise Information Systems, pp. 945–952 (2001)
Stumme, G., Madche, A.: FCA-Merge: Bottom-up merging of ontologies. In: Proc. of the 17th Int. Joint Conf. on AI, pp. 225–230 (2001)
Sun, A., Lim, E.: Hierarchical Text Classification and Evaluation. In: Proc. of IEEE Int. Conf. on Data Mining, pp. 521–528 (2001)
Yahoo! (2003), http://www.yahoo.com/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ichise, R., Hamasaki, M., Takeda, H. (2004). Discovering Relationships Among Catalogs. In: Suzuki, E., Arikawa, S. (eds) Discovery Science. DS 2004. Lecture Notes in Computer Science(), vol 3245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30214-8_33
Download citation
DOI: https://doi.org/10.1007/978-3-540-30214-8_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23357-2
Online ISBN: 978-3-540-30214-8
eBook Packages: Springer Book Archive