A Joint Classification Method to Integrate Scientific and Social Networks

  • Mahmood Neshati
  • Ehsaneddin Asgari
  • Djoerd Hiemstra
  • Hamid Beigy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7814)

Abstract

In this paper, we address the problem of scientific-social network integration to find a matching relationship between members of these networks. Utilizing several name similarity patterns and contextual properties of these networks, we design a focused crawler to find high probable matching pairs, then the problem of name disambiguation is reduced to predict the label of each candidate pair as either true or false matching. By defining matching dependency graph, we propose a joint label prediction model to determine the label of all candidate pairs simultaneously. An extensive set of experiments have been conducted on six test collections obtained from the DBLP and the Twitter networks to show the effectiveness of the proposed joint label prediction model.

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References

  1. 1.
    Balog, K., Azzopardi, L., de Rijke, M.: A language modeling framework for expert finding. Inf. Process. Manage. 45(1), 1–19 (2009)CrossRefGoogle Scholar
  2. 2.
    Serdyukov, P.: Search for expertise: going beyond direct evidence. PhD thesis, Enschede (June 2009)Google Scholar
  3. 3.
    Fang, Y., Si, L., Mathur, A.P.: Discriminative probabilistic models for expert search in heterogeneous information sources. Inf. Retr. 14, 158–177 (2011)CrossRefGoogle Scholar
  4. 4.
    Smirnova, E., Balog, K.: A User-Oriented Model for Expert Finding. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 580–592. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Deng, H., King, I., Lyu, M.R.: Enhanced models for expertise retrieval using community-aware strategies. IEEE Transactions on Systems, Man, and Cybernetics, Part B 42(1), 93–106 (2012)CrossRefGoogle Scholar
  6. 6.
    You, G.W., Park, J.W., Hwang, S.W., Nie, Z., Wen, J.R.: Socialsearchs+: enriching social network with web evidences. World Wide Web, 1–27 (2012)Google Scholar
  7. 7.
    Bhattacharya, I., Getoor, L.: Collective entity resolution in relational data. ACM Transactions on Knowledge Discovery from Data 1(1) (2007)Google Scholar
  8. 8.
    Taskar, B., Abbeel, P., Koller, D.: Discriminative probabilistic models for relational data. In: UAI, pp. 485–492 (2002)Google Scholar
  9. 9.
    Fang, Y., Si, L., Mathur, A.P.: Discriminative graphical models for faculty homepage discovery. Inf. Retr. 13(6), 618–635 (2010)CrossRefGoogle Scholar
  10. 10.
    Sutton, C., McCallum, A.: An Introduction to Conditional Random Fields for Relational Learning, pp. 93–128. MIT Press (2006)Google Scholar
  11. 11.
    Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML 2001, pp. 282–289. Morgan Kaufmann Publishers Inc., San Francisco (2001)Google Scholar
  12. 12.
    McCallum, A.: Efficiently inducing features of conditional random fields. In: Nineteenth Conference on Uncertainty in Artificial Intelligence, UAI 2003 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mahmood Neshati
    • 1
  • Ehsaneddin Asgari
    • 3
  • Djoerd Hiemstra
    • 2
  • Hamid Beigy
    • 1
  1. 1.Department of Computer EngineeringSharif University of TechnologyIran
  2. 2.Database Research GroupUniversity of TwenteThe Netherlands
  3. 3.School of Computer and Communication Science (IC)Ecole Polytechnique Fédéral de Lausanne - EPFLSwitzerland

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