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Representing, Querying and Transforming Social Networks with RDF/SPARQL

  • Mauro San Martín
  • Claudio Gutierrez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5554)

Abstract

As social networks are becoming ubiquitous on the Web, the Semantic Web goals indicate that it is critical to have a standard model allowing exchange, interoperability, transformation, and querying of social network data.

In this paper we show that RDF/SPARQL meet this desiderata. Building on developments of social network analysis, graph databases and Semantic Web, we present a social networks data model based on RDF, and a query and transformation language based on SPARQL meeting the above requirements. We study its expressive power and complexity showing that it behaves well, and present an illustrative prototype.

Keywords

Social Network Social Network Analysis Query Language Expressive Power Relational Algebra 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Mika, P.: Social Networks and the Semantic Web. Semantic Web And Beyond Computing for Human Experience, vol. 5. Springer, Heidelberg (2007)Google Scholar
  2. 2.
    Jung, J.J., Euzenat, J.: Towards semantic social networks. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 267–280. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Finin, T., Ding, L., Zou, L.: Social networking on the semantic web. Learning Organization Journal Ubiquitous Business Intelligence (2005)Google Scholar
  4. 4.
    Erétéo, G., et al.: A state of the art on social network analysis and its applications on a semantic web. In: SDoW 2008 at ISWC 2008 (2008)Google Scholar
  5. 5.
    Breiger, R.L.: The analysis of social networks. In: Hardy, M., Bryman, A. (eds.) Handbook of Data Analysis, pp. 505–526. Sage Publications, Thousand Oaks (2004)Google Scholar
  6. 6.
    Scott, J.: Social Network Analysis, 2nd edn. SAGE Publications, Thousand Oaks (2000)Google Scholar
  7. 7.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Structural Analysis in the Social Sciences. Cambridge University Press, Cambridge (1994)CrossRefzbMATHGoogle Scholar
  8. 8.
    Freeman, L., Romney, A.K., White, D.R. (eds.): Research Methods in Social Network Analysis. Transaction Publishers (1992)Google Scholar
  9. 9.
    de Nooy, W., Mrvar, A., Batagelj, V.: Exploratory Social Network Analysis with Pajek. Cambridge University Press, Cambridge (2005)CrossRefGoogle Scholar
  10. 10.
    Huisman, M., van Duijn, M.: Software for Social Network Analysis. In: [29], pp. 270–316Google Scholar
  11. 11.
    Handcock, M.S., Hunter, D.R., Butts, C.T., Goodreau, S.M., Morris, M.: Statnet: Software tools for the representation, visualization, analysis and simulation of network data. Journal of Statistical Software 24(1), 1–11 (2008)CrossRefGoogle Scholar
  12. 12.
    Butts, C.T.: Network: A package for managing relational data in R. Journal of Statistical Software 24(2), 1–36 (2008)CrossRefGoogle Scholar
  13. 13.
    Halpin, H.: Beyond walled gardens: Open standards for the social web. In: SDoW 2008 at ISWC 2008 (2008)Google Scholar
  14. 14.
    Freeman, L.: The Development of Social Network Analysis. Empirical Press (2004)Google Scholar
  15. 15.
    Angles, R., Gutierrez, C.: Survey of graph database models. ACM Computing Surveys (CSUR) 40(1), 1–39 (2008)CrossRefGoogle Scholar
  16. 16.
    Klink, S., Reuther, P., Weber, A., Walter, B., Ley, M.: Analysing social networks within bibliographical data. In: Bressan, S., Küng, J., Wagner, R. (eds.) DEXA 2006. LNCS, vol. 4080, pp. 234–243. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Carley, K.M.: Linking capabilities to needs. In: [36], pp. 363–370Google Scholar
  18. 18.
    Jagadish, H.V., Olken, F.: Database management for life science research: Summary report of the workshop on data management for molecular and cell biology. OMICS 7(1), 131–137 (2003)CrossRefGoogle Scholar
  19. 19.
    Gray, J., Liu, D., Nieto-Santisteban, M., Szalay, A., DeWitt, D., Heber, G.: Scientific data management in the coming decade. SIGMOD Record 34(4), 34–41 (2005)CrossRefGoogle Scholar
  20. 20.
    Jensen, D., Neville, J.: Data mining in social networks. In: [36], pp. 289–302Google Scholar
  21. 21.
    Blau, H., Immerman, N., Jensen, D.: A visual language for querying and updating graphs. CS Technical Report 2002-037, University of Massachusetts (2002)Google Scholar
  22. 22.
    Tsvetovat, M., Diesner, J., Carley, K.M.: Netintel: A database for manipulation of rich social network data. CMU-ISRI-04-135 (March 2005)Google Scholar
  23. 23.
    Tsvetovat, M., Reminga, J., Carley, K.M.: Dynetml: Interchange format for rich social network data. CMU-ISRI-04-105 (2004)Google Scholar
  24. 24.
    Polleres, A., Krennwallner, T., Lopes, N., Kopecký, J., Drecker, S.: XSPARQL language specification (2009), http://xsparql.deri.org/spec/
  25. 25.
    Aleman-Meza, B., Nagarajan, M., Ramakrishnan, C., Ding, L., Kolari, P., Sheth, A.P., Arpinar, I., Joshi, A., Finin, T.: Semantic analytics on social networks: Experiences in addressing the problem of conflict of interest detection. In: WWW 2006, pp. 407–416 (2006)Google Scholar
  26. 26.
    Kinsella, S., Harth, A., Troussov, A., Sogrin, M., Judge, J., Hayes, C., Breslin, J.G.: Navigating and annotating semantically-enabled networks of people and associated objects. In: ASNA 2007 (2007)Google Scholar
  27. 27.
    Mika, P.: Social networks and the semantic web. In: Web Intelligence, pp. 285–291. IEEE Computer Society, Los Alamitos (2004)Google Scholar
  28. 28.
    Bonacich, P., Cody Holdren, A., Johnston, M.: Hyper-edges and multidimensional centrality. Social Networks 26, 189–203 (2004)CrossRefGoogle Scholar
  29. 29.
    Carrington, P.J., Scott, J., Wasserman, S. (eds.): Models and Methods in Social Network Analysis. Cambridge (2005)Google Scholar
  30. 30.
    Gutierrez, C., Hurtado, C.A., Vaisman, A.: Introducing time into RDF. IEEE Transactions on Knowledge and Data Engineering 19(2), 207–218 (2007)CrossRefGoogle Scholar
  31. 31.
    Guting, R.: Graphdb: modeling and querying graphs in databases. In: 20th VLDB Conference, pp. 297–308 (1994)Google Scholar
  32. 32.
    Angles, R., Gutierrez, C.: The expressive power of SPARQL. In: Sheth, A., et al. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 114–129. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  33. 33.
    Courcelle, B.: Graph Rewriting: an Algebraic and Logic Approach. In: Handbook of Theoretical Computer Science. Formal Models and Sematics, vol. B, pp. 193–242. Elsevier and MIT Press (1990)Google Scholar
  34. 34.
    Pérez, J., Arenas, M., Gutierrez, C.: nSPARQL: A navigational language for RDF. In: ISWC 2008, pp. 66–81 (2008)Google Scholar
  35. 35.
    Alkhateeb, F., Baget, J., Euzenat, J.: Constrained regular expressions in SPARQL. In: SWWS 2008, pp. 91–99 (2008)Google Scholar
  36. 36.
    Breiger, R., Carley, K., Pattison, P. (eds.): Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers. The National Academies Press, Washington (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mauro San Martín
    • 1
    • 2
  • Claudio Gutierrez
    • 2
  1. 1.Departamento de MatemáticasUniversidad de La SerenaChile
  2. 2.Computer Science DepartmentUniversidad de ChileChile

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