Abstract
Assessing the similarity between node profiles in a social network is an important tool in its analysis. Several approaches exist to study profile similarity, including semantic approaches and natural language processing. However, to date there is no research combining these aspects into a unified measure of profile similarity. Traditionally, semantic similarity is assessed using keywords, that is, formatted text information, with no natural language processing component. This study proposes an alternative approach, whereby the similarity assessment based on keywords is applied to the output of natural language processing of profiles. A unified similarity measure results from this approach. The approach is illustrated on a real data set extracted from Facebook and compared with other similarity measures for the same data.
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References
The university of pennsylvania (penn) treebank tag-set, http://www.comp.leeds.ac.uk/ccalas/tagsets/upenn.html (accessed October 1, 2013)
Akcora, C.G., Carminati, B., Ferrari, E.: Network and profile based measures for user similarities on social networks. In: 2011 IEEE International Conference on Information Reuse and Integration (IRI), pp. 292–298. IEEE (2011)
Bhattacharyya, P., Garg, A., Wu, S.F.: Analysis of user keyword similarity in online social networks. Social Network Analysis and Mining 1(3), 143–158 (2011)
Boriah, S., Chandola, V., Kumar, V.: Similarity measures for categorical data: A comparative evaluation. Red 30(2), 3 (2008)
Bowes, R.: Return of the Facebook Snatchers (2012), http://www.skullsecurity.org/blog/2010/return-of-the-facebook-snatchers (accessed July 19, 2012)
Ellison, N.B., et al.: Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication 13(1), 210–230 (2007)
The Stanford Natural Language Processing Group. Pos Tagger FAQ, http://nlp.stanford.edu/software/pos-tagger-faq.shtml (accessed July 19, 2012)
Miller, G.A.: Wordnet: a lexical database for english. Communications of the ACM 38(11), 39–41 (1995)
Peat, H.J., Willett, P.: The limitations of term co-occurrence data for query expansion in document retrieval systems. JASIS 42(5), 378–383 (1991)
Spear, M., Lu, X., Matloff, N.S., Wu, S.F.: Inter-profile similarity (IPS): A method for semantic analysis of online social networks. In: Zhou, J. (ed.) Complex 2009. LNICST, vol. 4, pp. 320–333. Springer, Heidelberg (2009)
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Rawashdeh, A., Rawashdeh, M., Díaz, I., Ralescu, A. (2014). Measures of Semantic Similarity of Nodes in a Social Network. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2014. Communications in Computer and Information Science, vol 443. Springer, Cham. https://doi.org/10.1007/978-3-319-08855-6_9
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DOI: https://doi.org/10.1007/978-3-319-08855-6_9
Publisher Name: Springer, Cham
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