Analysis of social network data is gaining popularity with the increased availability of real-world data including data publicly available over the Internet such as publications and data resulting from interactions via social networking platforms and e-communication tools. In this chapter we present an approach to constructing light-weight ontologies from social network data. In relation to the more traditional (semi-)automatic ontology learning techniques we reuse the approach typically used in learning ontologies from text (see Grobelnik and Mladenić(2006) for details) where the candidate instances and classes for the ontology are lexical items described by a set of attributes. We replace lexical items with nodes in the social network and attributes by descriptions of the node context in the graph. Similar techniques can then be applied for ontology learning either from text or from social networks.
To prove our claims we perform experiment on a real life dataset taken from a mid-size organization (700–800 people). The dataset represents log files from organizational spam filter software giving us the set of e-mail transactions for a period of 19 months resulting in 2.7 million successful e-mail transactions used here for analysis and ontology learning.
The main contribution of this work is an architecture consisting of five major steps that enable transformation of the data from a set of e-mail transactions inside an organization to an ontology representing the structure of the organization.
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References
Aggarwal C, Yu P (2005) Online analysis of community evolution in data streams. In Proceedings of ACM SIAM on Data Mining
Albert R, Barabasi A L (1999) Emergence of scaling in random networks. Science, vol. 286, no. 5439, 509–12
Backstrom L, Huttenlocher D P, Kleinberg J M, Lan X (2005) Group formation in large social networks: membership, growth, and evolution. In Proceedings of 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Faloutsos M, Faloutsos P, Faloutsos C (1999) On power-law relationships of the internet topology. In SIGCOMM
Fortuna B, Mladenić D, Grobelnik M (2005) Visualization of text document corpus. Informatica (Ljublj.), vol. 29, no. 4, 497–5022. http://docatlas.ijs.si
Fortuna B, Mladenić D, Grobelnik M (2005a) Semi-automatic construction of topic ontology. Proceedings of the 8th International Multiconference Information Society IS 2005. Ljubljana: Institut “Jožef Stefan”, 170–173. http://ontogen.ijs.si
Grobelnik M, Mladenić D (2006) Knowledge discovery for ontology construction. In: Semantic web technologies: trends and research in ontology-based systems. Chichester: John Wiley & Sons, 9–27
Kumar R, Raghavan P, Rajagopalan S, Tomkins A (1999) Trawling the Web for emerging cyber-communities. In Proceedings of the Eighth World Wide Web Conference
Leskovec J, Kleinberg J, Faloutsos C (2005) Graphs over time: densification laws, shrinking diameters and possible explanations. In Proceedings of 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Mladenić D, Grobelnik M (2004) Visualizing very large graphs using clustering neighborhoods. In: Morik et al. (eds.), Local Pattern Detection: International Seminar. Dagstuhl Castle, Germany, April 12–16, revised selected papers, Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence, 3539, State-of-the-art survey. Berlin; Heidelberg; New York: Springer, 89–97
Wasserman S, Faust K (1994) Social network analysis: methods and applications. In: Structural Analysis in the Social Sciences. New York: Cambridge University Press
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Grobelnik, M., Mladenić, D., Fortuna, B. (2009). Ontology Generation from Social Networks. In: Davies, J., Grobelnik, M., Mladenić, D. (eds) Semantic Knowledge Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88845-1_10
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DOI: https://doi.org/10.1007/978-3-540-88845-1_10
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