Asocial tag clustering method based on common co-occurrence group similarity

Abstract

Social tagging systems are widely applied in Web 2.0. Many users use these systems to create, organize,manage, and share Internet resources freely. However, many ambiguous and uncontrolled tags produced by social tagging systems not only worsen users’ experience, but also restrict resources’ retrieval efficiency. Tag clustering can aggregate tags with similar semantics together, and help mitigate the above problems. In this paper, we first present a common co-occurrence group similarity based approach, which employs the ternary relation among users,resources, and tags to measure the semantic relevance between tags. Then we propose a spectral clustering method to address the high dimensionality and sparsity of the annotating data. Finally, experimental results show that the proposed method is useful and efficient.

Keywords

Social tagging systems / Tag co-occurrence / Spectral clustering / Group similarity http://dx.doi.org/10.1631/FITEE.1500187

References

[1] Begelman, G., Keller, P., Smadja, F., 2006. Automated tag clustering: improving search and exploration in the tag space. Proc. 15th Int. World Wide Web Conf., p.15–33.

[2] Bischoff, K., Firan, C.S., Nejdl, W., , 2008. Can all tags be used for search? Proc. 17th ACM Conf. on Information and Knowledge Management, p.193–202.

[3] Cui, J.W., Liu, H.Y., He, J., , 2011. TagClus: a random walk-based method for tag clustering. Knowl. Inform.Syst., 27(2):193–225.

[4] Cuzzocrea, A., 2006. Combining multidimensional user models and knowledge representation and management techniques for making web services knowledge-aware. Web Intell. Agent Syst., 4(3):289–312.

[5] Cuzzocrea, A., Mastroianni, C., 2003. A reference architecture for knowledge management-based web systems.Proc. 4th Int. Conf. on Web Information Systems Engineering, p.347–351.

[6] Dattolo, A.,Eynard, D., Mazzola, L., 2011. An integrated approach to discover tag semantics. Proc. ACM Symp.on Applied Computing, p.814–820.

[7] Deutsch, S., Schrammel, J., Tscheligi, M., 2011. Comparing different layouts of tag clouds: findings on visual perception.Human Aspects Visual., 6431:23–37.

[8] Dunn, J.C., 1974. Well-separated clusters and optimal fuzzypartitions.J. Cybern., 4(1):95–104.

[9] Furnas, G.W., Fake, C., von Ahn, L., , 2006. Why do tagging systems work? Proc. Extended Abstracts on Human Factors in Computing Systems, p.36–39.

[10] Gemmell, J., Shepitsen, A., Mobasher, B., , 2008. Personalizing navigation in folksonomies using hierarchical tag clustering. Proc. 10th Int. Conf. on Data Warehousing and Knowledge Discovery, p.196–205.

[11] Gu, M., Zha, H., Ding, C., , 2001. Spectral relaxation models and structure analysis for k-way graph clustering and bi-clustering. Available from http://citeseerx.ist.psu.edu/viewdoc/summary?Accessed on Apr. 5, 2015].

[12] Heymann, P., Garcia-Molina, H., 2006. Collaborative creation of communal hierarchical taxonomies in social tagging systems. Technical Report, No. 2006-10, Stanford University, USA.

[13] Isabella, P., 2009. Folksonomies. Indexing and Retrieval in Web 2.0. Walter de Gruyter, Berlin.

[14] Jiang, J.J., Conrath, D.W., 1997. Semantic similarity based on corpus statistics and lexical taxonomy. Proc. Int.Conf. of Research on Computational Linguistics, p.1–15.

[15] Kaufman, L., Rousseeuw, P.J., 2008. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons, London, UK.

[16] Knautz, K., Soubusta, S., Stock, W.G., 2010. Tag clusters as information retrieval interfaces. Proc. 43rd Hawaii Int. Conf. on System Sciences, p.1–10.

[17] Laniado, D., Eynard, D., Colombetti, M., 2007. Using Word-Net to turn a folksonomy into a hierarchy of concepts.Proc. 4th Italian Semantic Web Workshop on Semantic Web Application and Perspectives, p.192–201.

[18] Lehwark, P., Risi, S., Ultsch, A., 2008. Visualization and clustering of tagged music data. Proc. 31st Annual Conf. on Data Analysis, Machine Learning and Applications,p.673-680.

[19] Markines, B., Cattuto, C., Menczer, F., , 2009. Evaluating similarity measures for emergent semantics of social tagging. Proc. 18th Int. Conf. on World Wide Web,p.641-650.

[20] Marlow, C., Naaman, M., Boyd, D., , 2006. HT06,tagging paper, taxonomy, Flickr, academic article, to read. Proc. 17th Conf. on Hypertext and Hypermedia,p.31–40.

[21] Mathes, A., 2004. Folksonomies—cooperative classification and communication through shared metadata. Available from http://www.adammathes.com/academic/computer-mediated-communication/folksonomies.html[Accessed on Apr. 5, 2015].

[22] Michlmayr, E., Cayzer, S., 2007. Learning user profiles from tagging data and leveraging them for personal(ized)information access. Proc. 16th Int. World Wide Web Conf., p.1–7.

[23] Ng, A.Y., Jordan, M.I., Weiss, Y., 2002. On spectral clustering:analysis and an algorithm. Proc. Conf. Advances in Neural Information Processing Systems, p.849–856.

[24] Noll, M.G., Meinel, C., 2007. Web search personalization via social bookmarking and tagging. Proc. 6th Int.Semantic Web Conf. and 2nd Asian Semantic Web Conf. on the Semantic Web, p.367–380.

[25] Noruzi, A., 2006. Folksonomies: (un)controlled vocabulary?Knowl. Organ., 33(4):199–203.

[26] Rivadeneira, A.W., Gruen, D.M., Muller, M.J., , 2007.Getting our head in the clouds: toward evaluation studies of tagclouds. Proc. SIGCHI Conf. on Human Factors in Computing Systems, p.995–998.

[27] Salton, G., 1983. Introduction to Modern Information Retrieval.McGraw-Hill College, New York, USA.

[28] Shepitsen, A., Gemmell, J., Mobasher, B., , 2008. Personalized recommendation in social tagging systems using hierarchical clustering. Proc. ACM Conf. on Recommender Systems, p.259–266.

[29] Shi, J., Malik, J., 2000. Normalized cuts and image segmentation.IEEE Trans. Patt. Anal. Mach. Intell.,22(8):888–905.

[30] Shirky, C., 2004. Folksonomy.

[31] Simpson, E., 2008. Clustering tags in enterprise and web folksonomies. Proc. Int. Conf. on Weblogs and Social Media, p.222–223.

[32] Suchanek, F.M., Vojnovic, M., Gunawardena, D., 2008. Social tags: meaning and suggestions. Proc. 17th ACM Conf. on Information and Knowledge Management,p.223–232.

[33] Szomszor, M., Cattuto, C., Alani, H., , 2007. Folksonomies,the Semantic Web, and Movie Recommendation.Proc. 4th European Semantic Web Conf.,p.71–84.

[34] Van Damme, C., Hepp, M., Siorpaes, K., 2007. Folksontology:an integrated approach for turning folksonomies into ontologies. Proc. Workshop on Bridging the Gap Between Semantic Web and Web2.0, p.57–70.

[35] Vanderlei, T.A., Durāo F.A., Martins, A.C., , 2007.A cooperative classification mechanism for search and retrieval software components. Proc. ACM Symp. on Applied Computing, p.866–871.

[36] Vander Wal, T., 2004. Folksonomy.

[37] Vandic, D., van Dam, J.W., Hogenboom, F., , 2011.A semantic clustering-based approach for searching and browsing tag spaces. Proc. ACM Symp. on Applied Computing, p.1693–1699.

[38] Xu, G.D., Zong, Y., Jin, P., , 2015. KIPTC: a kernel information propagation tag clustering algorithm. J.Intell. Inform. Syst., 45(1):95–112.

RIGHTS & PERMISSIONS

2016 Zhejiang University and Springer-Verlag Berlin Heidelberg