Semanitic Keyword-based Search on Structured Data Sources

Semantic Keyword-based Search on Structured Data Sources pp 1-16 | Cite as

Professional Collaborative Information Seeking: On Traceability and Creative Sensemaking

  • Andreas Nürnberger
  • Dominic Stange
  • Michael Kotzyba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9398)

Abstract

The development of systems to support collaborative information seeking is a challenging issue for many reasons. Besides the support expected for an individual user, such as query formulation, relevance judgement, result set organization and summarization, the smooth exchange of search related information within the team of users seeking information has to be supported. This imposes strong requirements on visualization and interaction to enable user to easily trace and interpret the search activities of other team members and to jointly make sense of gathered information in order to solve the initial information need. In this paper, we briefly motivate specific requirements with a focus on collaborative professional search, review existing work and point out major challenges. In addition, we briefly introduce a system that has been specifically developed to support collaborative technology search.

Keywords

Collaborative search Information behaviour Search user interface 

References

  1. 1.
    Anderson, L.W., Krathwohl, D.R., Airasian, P.W., Cruikshank, K.A., Mayer, R.E., Pintrich, P.R., Raths, J., Wittrock, M.C.: A taxonomy for learning, teaching, and assessing: a revision of bloom’s taxonomy of educational objectives, 2nd edn. Allyn & Bacon, Boston (2001)Google Scholar
  2. 2.
    Aula, A., Russell, D.M.: Complex and exploratory web search. In: Information Seeking Support Systems (2008)Google Scholar
  3. 3.
    Basque, J., Pudelko, B.: Intersubjective meaning-making in dyads using object-typed concept mapping. In: Torres, P.L., Marriott, R.C.V. (eds.) Handbook of Research on Collaborative Learning Using Concept Mapping, Chapter 10, pp. 180–206. IGI Global, Pennsylvania (2010)Google Scholar
  4. 4.
    Byström, K., Järvelin, K.: Task complexity affects information seeking and use. Inf. Process. Manage. 31(2), 191–213 (1995)CrossRefGoogle Scholar
  5. 5.
    Capra, R., Chen, A.T., McArthur, E., Davis, N.: Searcher actions and strategies in asynchronous collaborative search. In: Proceedings of 76th ASIS&T Annual Meeting: Beyond the Cloud: Rethinking Information Boundaries, pp. 75:1–75:10 (2013)Google Scholar
  6. 6.
    Capra, R., Marchionini, G., Velasco-Martin, J., Muller, K.: Tools-at-hand and learning in multi-session, collaborative search. In: Proceedings of SIGCHI Conference on Human Factors in Computing Systems, pp. 951–960. ACM (2010)Google Scholar
  7. 7.
    Ellis, D.: A behavioral approach to information retrieval system design. J. Documentation 45(3), 171–212 (1989)CrossRefGoogle Scholar
  8. 8.
    Ellis, D., Cox, D., Hall, K.: A comparison of the information seeking patterns of researchers in the physical and social sciences. J. Documentation 49(4), 356–369 (1993)CrossRefGoogle Scholar
  9. 9.
    Ellis, D., Haugan, M.: Modelling the information seeking patterns of engineers and research scientists in an industrial environment. J. Documentation 53(4), 384–403 (1997)CrossRefGoogle Scholar
  10. 10.
    Evans, B.M., Chi, E.H.: Towards a model of understanding social search. In: Proceedings of ACM Conference on Computer Supported Cooperative Work, pp. 485–494. ACM (2008)Google Scholar
  11. 11.
    Gäde, M., Hall, M.M., Huurdeman, H., Kamps, J., Koolen, M., Skov, M., Toms, E., Walsh, D.: First workshop on supporting complex search tasks. In: Proceedings of the First International Workshop on Supporting Complex Search Tasks, part of ECIR (2015)Google Scholar
  12. 12.
    Golovchinsky, G., Qvarfordt, P., Pickens, J.: Collaborative information seeking. Computer 42(3), 47–51 (2009)CrossRefGoogle Scholar
  13. 13.
    Gossen, T., Bade, K., Nürnberger, A.: A comparative study of collaborative and individual web search for a social planning task. In: Proceedings of LWA Workshop (2011)Google Scholar
  14. 14.
    Hearst, M.A.: What’s missing from collaborative search? Computer 47(3), 58–61 (2014)CrossRefGoogle Scholar
  15. 15.
    Hembrooke, H.A., Granka, L.A., Gay, G.K., Liddy, E.D.: The effects of expertise and feedback on search term selection and subsequent learning: research articles. J. Am. Soc. Inf. Sci. Technol. 56(8), 861–871 (2005)CrossRefGoogle Scholar
  16. 16.
    Kelly, R., Payne, S.J.: Collaborative web search in context: A study of tool use in everyday tasks. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & #38; Social Computing, CSCW 2014, pp. 807–819. ACM, New York, NY, USA (2014)Google Scholar
  17. 17.
    Knight, S.A., Spink, A.: Toward a web search information behavior model. In: Spink, A., Zimmer, M. (eds.) Web Search. Information Science and Knowledge Management, vol. 14, pp. 209–234. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Kotov, A., Bennett, P.N., White, R.W., Dumais, S.T., Teevan, J.: Modeling and analysis of cross-session search tasks. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 5–14. ACM (2011)Google Scholar
  19. 19.
    Kuhlthau, C.C.: Inside the search process: information seeking from the user’s perspective. J. Am. Soc. Inf. Sci. 42(5), 361–371 (1991)CrossRefGoogle Scholar
  20. 20.
    Kuhlthau, C.C.: Seeking Meaning: A Process Approach to Library and Information Services. Ablex Publishing, Norwood, NJ (1994)Google Scholar
  21. 21.
    Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM 49(4), 41–46 (2006)CrossRefGoogle Scholar
  22. 22.
    Morris, M.R.: Interfaces for collaborative exploratory web search: motivations and directions for multi-user designs. In: CHI 2007 Workshop on Exploratory Search and HCI (2007)Google Scholar
  23. 23.
    Morris, M.R.: Collaborating alone and together: investigating persistent and multi-user web search activities, Technical report MSR-TR-2007-11, Microsoft Research (2007)Google Scholar
  24. 24.
    Morris, M.R., Teevan, J., Bush, S.: Enhancing collaborative web search with personalization: groupization, smart splitting, and group hit-highlighting. In: Proceedings of ACM Conference on Computer Supported Cooperative Work, pp. 481–484. ACM (2008)Google Scholar
  25. 25.
    Poltrock, S.E., Grudin, J., Dumais, S.T., Fidel, R., Bruce, H., Pejtersen, A.M.: Information seeking and sharing in design teams. In: Schmidt, K., Pendergast, M., Tremaine, M., Simone, C. (eds.) GROUP, pp. 239–247. ACM, New York (2003)Google Scholar
  26. 26.
    Reddy, M.C., Jansen, B.J.: A model for understanding collaborative information behavior in context: a study of two healthcare teams. Inf. Process. Manage. 44(1), 256–273 (2008)CrossRefGoogle Scholar
  27. 27.
    Russell, D.M., Stefik, M.J., Pirolli, P., Card, S.K.: The cost structure of sensemaking. In: Proceedings of INTERACT 1993 and CHI 1993 Conference on Human Factors in Computing Systems, pp. 269–276. ACM (1993)Google Scholar
  28. 28.
    Shah, C.: Collaborative information seeking: a literature review. In: 2009 Workshop on Collaborative Information Behavior (2009)Google Scholar
  29. 29.
    Shah, C.: Collaborative Information Seeking - The Art and Science of Making the Whole Greater than the Sum of All, vol. 34. Springer, Heidelberg (2012)Google Scholar
  30. 30.
    Shah, C.: Collaborative information seeking. J. Assoc. Inf. Sci. Technol. 65(2), 215–236 (2014)CrossRefGoogle Scholar
  31. 31.
    Stange, D., Nürnberger, A.: Search maps: enhancing traceability and overview in collaborative information seeking. In: de Rijke, M., Kenter, T., de Vries, A.P., Zhai, C.X., de Jong, F., Radinsky, K., Hofmann, K. (eds.) ECIR 2014. LNCS, vol. 8416, pp. 763–766. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  32. 32.
    Stange, D., Nürnberger, A.: When experts collaborate: sharing search and domain expertise within an organization. In: Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business, ACM, New York, NY, USA (2015, to appear)Google Scholar
  33. 33.
    White, R.W., Dumais, S.T., Teevan, J.: Characterizing the influence of domain expertise on web search behavior. In: Proceedings of the Second ACM International Conference on Web Search and Data Mining, pp. 132–141. ACM (2009)Google Scholar
  34. 34.
    Wilson, T.D.: On user studies and information needs. J. Documentation 37(1), 3–15 (1981)CrossRefGoogle Scholar
  35. 35.
    Wilson, T.D.: Models in information behaviour research. J. Documentation 55(3), 249–270 (1999)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Andreas Nürnberger
    • 1
  • Dominic Stange
    • 2
  • Michael Kotzyba
    • 1
  1. 1.DKE Group, Faculty of Computer ScienceUniversity of MagdeburgMagdeburgGermany
  2. 2.Volkswagen AGWolfsburgGermany

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