Abstract
Search results clustering is useful for clarifying vague queries and in managing the sheer volume of web pages. But these clusters are often incomprehensible to users. In this paper, we propose a new method for producing intuitive clusters that greatly aid in finding desired web search results. By using terms that are both frequently used in queries and found together on web pages to build clusters our method combines the better features of both “computer-oriented clustering” and “human-oriented clustering”. Our evaluation experiments show that this method provides the user with appropriate clusters and clear labels.
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
Baeza-Yates, R.A., Hurtado, C.A., Mendoza, M.: Query Recommendation Using Query Logs in Search Engines. In: Lindner, W., Mesiti, M., Türker, C., Tzitzikas, Y., Vakali, A.I. (eds.) EDBT 2004. LNCS, vol. 3268, pp. 588–596. Springer, Heidelberg (2004)
Zhang, Z., Nasraoui, O.: Mining search engine query logs for query recommendation. In: WWW 2006, pp. 1039–1040 (2006)
Zamir, O., Etzioni, O.: Web Document Clustering: A Feasibility Demonstration. In: SIGIR 1998, pp. 46–54 (1998)
Kohonen, T.: Self-Organizing Maps of Massive Document Collections. In: IJCNN 2000, vol. 2, pp. 3–12 (2000)
Osinski, S.: Improving Quality of Search Results Clustering with Approximate Matrix Factorisations. In: ECIR 2006, pp. 167–178 (2006)
Beeferman, D., Berger, A.L.: Agglomerative clustering of a search engine query log. In: KDD 2000, pp. 407–416 (2000)
Wen, J.R., Nie, J.Y., Zhang, H.J.: Query clustering using user logs. ACM Trans. Inf. Syst. 20(1), 59–81 (2002)
Iwayama, M., Tokunaga, T.: Hierarchical Bayesian Clustering for Automatic Text Classification. In: IJCAI 1995, pp. 1322–1327 (1995)
Tokunaga, T., Iwayama, M., Tanaka, H.: Automatic Thesaurus Construction based on Grammatical Relations. In: IJCAI 1995, pp. 1308-1313 (1995)
Schutze, H., Pedersen, J.: A Cooccurrence-Based Thesaurus and Two Applications to Information Retrieval. Inf. Process. Manage. 33(3), 307–318 (1997)
Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing. Mit. Pr. (1999)
Kaki, M.: Optimizing the number of search result categories. In: CHI 2005, pp. 1517–1520 (2005)
Stemmer, P.: http://tartarus.org/~martin/PorterStemmer/index.html
Eguchi, K., Oyama, K., Ishida, E., Kando, N., Kuriyama, K.: NTCIR-3 WEB: An Evaluation Workshop for Web Retrieval. NII Journal 6, 31–56 (2003)
Yasukawa, M., Yokoo, H.: Web Search Based on Clustering of Related Terms Acquired from Search Log (Japanese). IEICE Trans. Inf. Syst. J90-D(2), 269–280 (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yasukawa, M., Yokoo, H. (2007). Related Terms Clustering for Enhancing the Comprehensibility of Web Search Results. In: Wagner, R., Revell, N., Pernul, G. (eds) Database and Expert Systems Applications. DEXA 2007. Lecture Notes in Computer Science, vol 4653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74469-6_36
Download citation
DOI: https://doi.org/10.1007/978-3-540-74469-6_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74467-2
Online ISBN: 978-3-540-74469-6
eBook Packages: Computer ScienceComputer Science (R0)