High Quality Expertise Evidence for Expert Search

  • Craig Macdonald
  • David Hannah
  • Iadh Ounis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4956)

Abstract

In an Enterprise setting, an expert search system can assist users with their “expertise need” by suggesting people with relevant expertise to the topic of interest. These systems typically work by associating documentary evidence of expertise to each candidate expert, and then ranking the candidates by the extent to which the documents in their profile are about the query. There are three important factors that affect the retrieval performance of an expert search system - firstly, the selection of the candidate profiles (the documents associated with each candidate), secondly, how the topicality of the documents is measured, and thirdly how the evidence of expertise from the associated documents is combined. In this work, we investigate a new dimension to expert finding, namely whether some documents are better indicators of expertise than others in each candidate’s profile. We apply five techniques to predict the quality documents in candidate profiles, which are likely to be good indicators of expertise. The techniques applied include the identification of possible candidate homepages, and of clustering the documents in each profile to determine the candidate’s main areas of expertise. The proposed approaches are evaluated on three expert search task from recent TREC Enterprise tracks and provide conclusions.

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References

  1. 1.
    Craswell, N., Hawking, D., Vercoustre, A.M., Wilkins, P.: Panoptic expert: Searching for experts not just for documents. In: AusWeb Poster Proceedings, Queensland, Australia (2001)Google Scholar
  2. 2.
    Liu, X., Croft, W.B., Koll, M.: Finding experts in community-based question-answering services. In: Proceedings of CIKM 2005, pp. 315–316 (2005)Google Scholar
  3. 3.
    Macdonald, C., Ounis, I.: Voting for candidates: Adapting data fusion techniques for an expert search task. In: Proceedings of CIKM 2006 (2006)Google Scholar
  4. 4.
    Craswell, N., de Vries, A.P., Soboroff, I.: Overview of the TREC-2005 Enterprise Track. In: Proceedings of TREC-2005, pp. 199–204 (2006)Google Scholar
  5. 5.
    Balog, K., Azzopardi, L., de Rijke, M.: Formal models for expert finding in enterprise corpora. In: Proceedings of SIGIR 2006, pp. 43–50 (2006)Google Scholar
  6. 6.
    Petkova, D., Croft, W.B.: Hierarchical language models for expert finding in enterprise corpora. In: Proceedings of ICTAI 2006, pp. 599–608 (2006)Google Scholar
  7. 7.
    Macdonald, C., Ounis, I.: Voting Techniques for Expert Search. J. Knowledge and Information Systems (in press, 2008), DOI:10.1007/s10115-007-0105-3Google Scholar
  8. 8.
    Macdonald, C., Ounis, I.: Using Relevance Feedback in Expert Search. In: Amati, G., Carpineto, C., Romano, G. (eds.) ECIR 2007. LNCS, vol. 4425, pp. 431–443. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Macdonald, C., Ounis, I.: Expertise drift and query expansion in expert search. In: Proceedings of CIKM 2007 (in press, 2007)Google Scholar
  10. 10.
    Lioma, C., Macdonald, C., Plachouras, V., Peng, J., He, B., Ounis, I.: University of Glasgow at TREC 2006: Experiments in Terabyte and Enterprise Tracks with Terrier. In: Proceedings of TREC 2006 (2007)Google Scholar
  11. 11.
    Balog, K., de Rijke, M.: Finding experts and their details in e-mail corpora. In: Proceedings of WWW 2006 (2006)Google Scholar
  12. 12.
    Macdonald, C., Ounis, I.: Voting for Experts: The Voting Model for Expert Search. Special issue of the Computer Journal on Expertise Profiling (to appear, 2008)Google Scholar
  13. 13.
    Kraaij, W., Westerveld, T., Hiemstra, D.: The importance of prior probabilities for entry page search. In: Proceedings of SIGIR 2002, pp. 27–34 (2002)Google Scholar
  14. 14.
    Craswell, N., Robertson, S., Zaragoza, H., Taylor, M.: Relevance weighting for query independent evidence. In: Proceedings of SIGIR 2005, pp. 416–423 (2005)Google Scholar
  15. 15.
    Fang, H., Zhai, C.: Probabilistic models for Expert Finding. In: Amati, G., Carpineto, C., Romano, G. (eds.) ECIR 2007. LNCS, vol. 4425, pp. 418–430. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Cao, Y., Li, H., Liu, J., Bao, S.: Research on expert search at enterprise track of TREC 2005. In: Proceedings of TREC-2005 (2006)Google Scholar
  17. 17.
    Amati, G.: Frequentist and Bayesian approach to information retrieval. In: Lalmas, M., MacFarlane, A., Rüger, S.M., Tombros, A., Tsikrika, T., Yavlinsky, A. (eds.) ECIR 2006. LNCS, vol. 3936, pp. 13–24. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Hawking, D., Craswell, N., Crimmins, F., Upstill, T.: How valuable is external link evidence when searching enterprise webs? In: Proceedings of ADC 2004, pp. 77–84 (2004)Google Scholar
  19. 19.
    Fagin, R., Kumar, R., McCurley, K.S., Novak, J., Sivakumar, D., Tomlin, J.A., Williamson, D.P.: Searching the workplace web. In: Proceedings of WWW 2003, pp. 366–375 (2003)Google Scholar
  20. 20.
    Peng, J., Macdonald, C., He, B., Ounis, I.: Combination of Document Priors in Web Information Retrieval. In: Proceedings of RIAO 2007 (2007)Google Scholar
  21. 21.
    Ounis, I., Amati, G., Plachouras, V., He, B., Macdonald, C., Lioma, C.: Terrier: A high performance and scalable information retrieval platform. In: Proceedings of the OSIR Workshop 2006, pp. 18–25 (2006)Google Scholar
  22. 22.
    Macdonald, C., Ounis, I.: Expert Search Evaluation by Supporting Documents. In: Macdonald, C., et al. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 555–563. Springer, Heidelberg (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Craig Macdonald
    • 1
  • David Hannah
    • 1
  • Iadh Ounis
    • 1
  1. 1.University of GlasgowUK

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