Abstract
Social networks are in constant growth, here users share all kind of information such as news, pictures and their personal opinions about different topics. In order to a user can retrieve such content for a topic of interest, it must provide the terms believed to occur in the posts; but in a matter of semantics, this tends to leave out relevant results. This paper proposes an approach to perform semantic classification of posts in social networks using concept hierarchies (CH). This classification is considered as a first step towards semantic searching. In addition, a method to obtain a CH for a particular subject is also proposed. With the implementation of this approach, the obtained results reflect what it seems to be a so promising approach, obtaining more than 64% of accuracy on the F-measure.
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References
Waitelonis, J., Sack, H.: Towards exploratory video search using linked data. In: 11th IEEE International Symposium on Multimedia, ISM 2009, pp. 540–545 (2009)
Hausenblas, M.: Exploiting linked data to build web applications. IEEE Internet Computing 13, 68–73 (2009)
Pushpa, S., Easwarakumar, K.S., Elias, S., Maamar, Z.: Referral based expertise search system in a time evolving social network. In: Proceedings of the Third Annual ACM Bangalore Conference, COMPUTE 2010, pp. 6:1– 6:8. ACM, New York (2010)
Yu, B., Singh, M.P.: Searching social networks. In: Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2003, pp. 65–72. ACM, New York (2003)
dos Reis, J.C., Bonacin, R., Baranauskas, M.C.C.: Beyond the Social Search: Personalizing the Semantic Search in Social Networks. In: Ozok, A.A., Zaphiris, P. (eds.) OCSC 2011. LNCS, vol. 6778, pp. 345–354. Springer, Heidelberg (2011)
Yeh, J.-H., Sie, S.-H.: Towards Automatic Concept Hierarchy Generation for Specific Knowledge Network. In: Ali, M., Dapoigny, R. (eds.) IEA/AIE 2006. LNCS (LNAI), vol. 4031, pp. 982–989. Springer, Heidelberg (2006)
Kuo, H.C., Lai, H.C., Huang, J.P.: Building a concept hierarchy automatically and its measuring. In: 2008 International Conference on Machine Learning and Cybernetics, vol. 7, pp. 3975–3978 (2008)
Qiao, S., Chunhui, Z., Zhibo, C.: Automatic construction of domain concept hierarchy. In: 2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), pp. 433–436 (2010)
Sun, A., Lim, E.P.: Hierarchical text classification and evaluation. In: Proceedings of the 2001 IEEE International Conference on Data Mining, ICDM 2001, pp. 521–528. IEEE Computer Society, Washington, DC (2001)
Gelbukh, A., Sidorov, G., Guzmán-Arenas, A.: Document indexing with a concept hierarchy. In: In New Developments in Digital Libraries. In: Proceedings of the 1st International Workshop on New Developments in Digital Libraries (NDDL 2001). ICEIS Press, Setubal (2001)
Chein, M., Mugnier, M.L.: Graph-based Knowledge Representation: Computational Foundations of Conceptual Graphs, 1st edn. Springer Publishing Company (2009) (Incorporated)
Cilibrasi, R., Vitanyi, P.: The google similarity distance. IEEE Transactions on Knowledge and Data Engineering 19, 370–383 (2007)
Kiritchenko, S., Matwin, S., Famili, A.F.: Functional annotation of genes using hierarchical text categorization. In: BioLINK SIG: Linking Literature, Information and Knowledge for Biology (2005)
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Ruiz-Mireles, K., Lopez-Arevalo, I., Sosa-Sosa, V. (2013). Semantic Classification of Posts in Social Networks by Means of Concept Hierarchies. In: Batyrshin, I., Mendoza, M.G. (eds) Advances in Computational Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37798-3_10
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DOI: https://doi.org/10.1007/978-3-642-37798-3_10
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