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Breaking the Recursivity: Towards a Model to Analyse Expert Finders

  • Matthieu Vergne
  • Angelo Susi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9381)

Abstract

Expert Finding (EF) techniques help in discovering people having relevant knowledge and skills. But for their validation, EF techniques usually rely on experts, meaning using another EF technique, generally not properly validated, and exploit them mainly for output validations, meaning only at late stages. We propose a model, which builds on literature in Psychology and practice, to identify generic concepts and relations in order to support the analysis and design of EF techniques, thus inferring potential improvements during early stages in an expert-free manner. Our contribution lies in the identification and review of relevant literature, building the conceptual model, and illustrating its use through an analysis of existing EF techniques. Although the model can be improved, we can already identify strengths and limitations in recent EF techniques, thus supporting the usefulness of a model-based analysis and design for EF techniques.

Keywords

Expert finding Concept formalization Model-driven analysis Design support 

References

  1. 1.
    Chi, M.T.H.: Two approaches to the study of experts’ characteristics. In: Ericsson, K.A., Charness, N., Feltovich, P.J., Hoffman, R.R. (eds.) The Cambridge Handbook of Expertise and Expert Performance, pp. 21–30. Cambridge University Press, New York (2006)CrossRefGoogle Scholar
  2. 2.
    Ericsson, K.A.: The influence of experience and deliberate practice on the development of superior expert performance. In: Ericsson, K.A., Charness, N., Feltovich, P.J., Hoffman, R.R. (eds.) The Cambridge Handbook of Expertise and Expert Performance, pp. 683–703. Cambridge University Press, New York (2006)CrossRefGoogle Scholar
  3. 3.
    Ericsson, K.A.: An Introduction to cambridge handbook of expertise and expert performance: its development, organization, and content. In: Ericsson, K.A., Charness, N., Feltovich, P.J., Hoffman, R.R. (eds.) The Cambridge Handbook of Expertise and Expert Performance. Cambridge University Press, New York (2006)CrossRefGoogle Scholar
  4. 4.
    Ericsson, K.A.: Creative expertise as superior reproducible performance: innovative and flexible aspects of expert performance. Psychol. Inquiry 10(4), 329–333 (1999)Google Scholar
  5. 5.
    Ericsson, K.A., Krampe, R.T., Tesch-romer, C.: The role of deliberate practice in the acquisition of expert performance. Psychol. Rev. 100(3), 363–406 (1993)CrossRefGoogle Scholar
  6. 6.
    Fazel-Zarandi, M., Fox, M.S., Yu, E.: Ontologies in expertise finding systems: modeling, analysis, and design. In: Ahmad, M.N., Colomb, R.M., Abdullah, M.S. (eds.) Ontology-Based Applications for Enterprise Systems and Knowledge Management, pp. 158–177. IGI Global, Hershey (2013)CrossRefGoogle Scholar
  7. 7.
    Karimzadehgan, M., White, R.W., Richardson, M.: Enhancing expert finding using organizational hierarchies. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Maybury, M.T.: Expert Finding Systems. MITRE Center for Integrated Intelligence Systems, Bedford (2006)Google Scholar
  9. 9.
    McDonald, D.W., Ackerman, M.S.: Just talk to me: a field study of expertise location. In: Proceedings of the Conference on CSCW, pp. 315–324. ACM, New York (1998)Google Scholar
  10. 10.
    Mockus, A., Herbsleb, J.D.: Expertise browser: a quantitative approach to identifying expertise. In: Proceedings of the 24th ICSE, pp. 503–512. ACM, New York (2002)Google Scholar
  11. 11.
    Mohebzada, J., Ruhe, G., Eberlein, A.: Systematic mapping of recommendation systems for requirements engineering. In: 2012 ICSSP, pp. 200–209 (2012)Google Scholar
  12. 12.
    Serdyukov, P., Hiemstra, D.: Modeling documents as mixtures of persons for expert finding. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 309–320. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  13. 13.
    Sonnentag, S., Niessen, C., Volmer, J.: Expertise in software design. In: Ericsson, K.A., Charness, N., Feltovich, P.J., Hoffman, R.R. (eds.) The Cambridge Handbook of Expertise and Expert Performance. Cambridge University Press, New York (2006) Google Scholar
  14. 14.
    Tang, J., Zhang, D., Yao, L.: Social network extraction of academic researchers. In: 7th IEEE ICDM, pp. 292–301 (2007)Google Scholar
  15. 15.
    Vergne, M., Susi, A.: Expert finding using Markov networks in open source communities. In: Jarke, M., Mylopoulos, J., Quix, C., Rolland, C., Manolopoulos, Y., Mouratidis, H., Horkoff, J. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 196–210. Springer, Heidelberg (2014) Google Scholar
  16. 16.
    Yimam-Seid, D., Kobsa, A.: Expert-finding systems for organizations: problem and domain analysis and the DEMOIR approach. J. Organ. Comput. Electron. Commer. 13(1), 1–24 (2003)Google Scholar
  17. 17.
    Zhang, J., Ackerman, M.S., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: Proceedings of the 16th International Conference on WWW, pp. 221–230. ACM, New York (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Center for Information and Communication Technology, FBK-ICTPovoItaly
  2. 2.Doctoral School in Information and Communication TechnologyPovoItaly

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