Abstract
By amassing ‘wisdom of the crowd’, social tagging systems draw more and more academic attention in interpreting Internet folk knowledge. In order to uncover their hidden semantics, several researches have attempted to induce an ontology-like taxonomy from tags. As far as we know, these methods all need to compute an overall or relative generality for each tag, which is difficult and error-prone. In this paper, we propose an agglomerative hierarchical clustering framework which relies only on how similar every two tags are. We enhance our framework by integrating it with a topic model to capture thematic correlations among tags. By experimenting on a designated online tagging system, we show that our method can disclose new semantic structures that supplement the output of previous approaches. Finally, we demonstrate the effectiveness of our method with quantitative evaluations.
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References
Staab, S., Studer, R. (eds.): Handbook on Ontologies, 2nd edn. Springer, Berlin (2009)
Liu, K., Fang, B., Zhang, W.: Ontology emergence from folksonomies. In: Huang, J., Koudas, N., Jones, G.J.F., Wu, X., Collins-Thompson, K., An, A. (eds.) CIKM, pp. 1109–1118. ACM (2010)
Tang, J., Leung, H.-F., Luo, Q., Chen, D., Gong, J.: Towards ontology learning from folksonomies. In: Boutilier, C. (ed.) IJCAI, pp. 2089–2094 (2009)
Wang, W., Barnaghi, P.M., Bargiela, A.: Probabilistic topic models for learning terminological ontologies. IEEE Trans. Knowl. Data Eng. 22(7), 1028–1040 (2010)
Navigli, R., Velardi, P., Faralli, S.: A graph-based algorithm for inducing lexical taxonomies from scratch. In: Walsh, T. (ed.) IJCAI, pp. 1872–1877. IJCAI/AAAI (2011)
Russell, S.J., Norvig, P.: Artificial Intelligence - A Modern Approach, 3rd internat edn. Pearson Education (2010)
Heymann, P., Garcia-Molina, H.: Collaborative creation of communal hierarchical taxonomies in social tagging systems. Technical report, Computer Science Department, Standford University (April 2006)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Itti, L., Baldi, P.: Bayesian surprise attracts human attention. In: NIPS (2005)
Brooks, C.H., Montanez, N.: Improved annotation of the blogosphere via autotagging and hierarchical clustering. In: Carr, L., Roure, D.D., Iyengar, A., Goble, C.A., Dahlin, M. (eds.) WWW, pp. 625–632. ACM (2006)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley (2005)
Blei, D.M., McAuliffe, J.D.: Supervised topic models. In: Platt, J.C., Koller, D., Singer, Y., Roweis, S.T. (eds.) NIPS. Curran Associates, Inc. (2007)
Blei, D.M., Griffiths, T.L., Jordan, M.I.: The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies. J. ACM 57(2) (2010)
Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proceedings of the National Academy of Science 101, 5228–5235 (2004)
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Li, X. et al. (2012). Inducing Taxonomy from Tags: An Agglomerative Hierarchical Clustering Framework. 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_6
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DOI: https://doi.org/10.1007/978-3-642-35527-1_6
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