Abstract
Recent research on Web page clustering has shown that the user query plays a critical role in guiding the categorisation of web search results. This paper combines our Query Directed Clustering algorithm (QDC) with another existing algorithm, Suffix Tree Clustering (STC), to identify common phrases shared by documents for base cluster identification. One main contribution is the utilising of a new Wikipedia link based measure to estimate the semantic relatedness between query and the base cluster labels, which has shown great promise in identifying the good base clusters. Our experimental results show that the performance is improved by utilising suffix trees and Wikipedia links.
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
Zamir, O., Etzioni, O.: Web document clustering: a feasibility demonstration. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1998, pp. 46–54. ACM, New York (1998)
Crabtree, D., Andreae, P., Gao, X.: Query directed web page clustering. In: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2006, pp. 202–210. IEEE Computer Society, Washington, DC (2006)
Crabtree, D., Gao, X., Andreae, P.: Query directed clustering. The Knowledge and Information Systems (KAIS) Journal (acepted July 29, 2012)
Cilibrasi, R.L., Vitanyi, P.M.B.: The google similarity distance. IEEE Trans. on Knowl. and Data Eng. 19, 370–383 (2007)
Milne, D., Witten, I.H.: An effective, low-cost measure of semantic relatedness obtained from wikipedia links. In: Proceedings of AAAI 2008 (2008)
Weiner, P.: Linear pattern matching algorithms. In: Proceedings of the 14th Annual Symposium on Switching and Automata Theory (SWAT 1973), pp. 1–11. IEEE Computer Society, Washington, DC (1973)
Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 1606–1611 (2007)
Strube, M., Ponzetto, S.P.: Wikirelate! computing semantic relatedness using wikipedia. In: Proceedings of Association for the Advancement of Artificial Intelligence, AAAI (2006)
Bu, F., Hao, Y., Zhu, X.: Semantic relationship discovery with wikipedia structure. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI 2011, vol. 3, pp. 1770–1775. AAAI Press (2011)
Crabtree, D.: Raw data set (2005), http://www.danielcrabtree.com/research/wi05/rawdata.zip
Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, IJCAI 2007, pp. 1606–1611. Morgan Kaufmann Publishers Inc., San Francisco (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Park, J., Gao, X., Andreae, P. (2012). Query Directed Web Page Clustering Using Suffix Tree and Wikipedia Links. In: Zhou, S., Zhang, S., Karypis, G. (eds) Advanced Data Mining and Applications. ADMA 2012. Lecture Notes in Computer Science(), vol 7713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35527-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-35527-1_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35526-4
Online ISBN: 978-3-642-35527-1
eBook Packages: Computer ScienceComputer Science (R0)