A Novel Combined Term Suggestion Service for Domain-Specific Digital Libraries

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6966)


Interactive query expansion can assist users during their query formulation process. We conducted a user study with over 4,000 unique visitors and four different design approaches for a search term suggestion service. As a basis for our evaluation we have implemented services which use three different vocabularies: (1) user search terms, (2) terms from a terminology service and (3) thesaurus terms. Additionally, we have created a new combined service which utilizes thesaurus term and terms from a domain-specific search term recommender. Our results show that the thesaurus-based method clearly is used more often compared to the other single-method implementations. We interpret this as a strong indicator that term suggestion mechanisms should be domain-specific to be close to the user terminology. Our novel combined approach which interconnects a thesaurus service with additional statistical relations outperformed all other implementations. All our observations show that domain-specific vocabulary can support the user in finding alternative concepts and formulating queries.


Evaluation Term Suggestion Query Suggestion Thesaurus Digital Libraries Interactive Query Expansion 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aula, A., Jhaveri, N., Käki, M.: Information search and re-access strategies of experienced web users. In: Proceedings of the 14th International Conference on World Wide Web, pp. 583–592. ACM, New York (2005)CrossRefGoogle Scholar
  2. 2.
    Belkin, N.J.: Anomalous states of knowledge as a basis for information retrieval. Canadian Journal of Information Science 5, 133–143 (1980)Google Scholar
  3. 3.
    Bhogal, J., Macfarlane, A., Smith, P.: A review of ontology based query expansion. Inf. Process. Manage. 43, 866–886 (2007)CrossRefGoogle Scholar
  4. 4.
    Cucerzan, S., Brill, E.: Spelling Correction as an Iterative Process that Exploits the Collective Knowledge of Web Users. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP (2004)Google Scholar
  5. 5.
    Efthimiadis, E.N.: Query expansion. Annual Review of Information Systems and Technology (ARIST) 31, 121–187 (1996)Google Scholar
  6. 6.
    Furnas, G.W., Landauer, T.K., Gomez, L.M., Dumais, S.T.: The Vocabulary Problem in Human-System Communication. Commun. ACM 30(11), 964–971 (1987)CrossRefGoogle Scholar
  7. 7.
    Hargittai, E.: Hurdles to information seeking: Spelling and typographical mistakes during users’ online behavior. Journal of the Association of Information Systems 7(1), 52–67 (2006)Google Scholar
  8. 8.
    Hearst, M.: Search User Interaces. Cambridge University Press, Cambridge (2009)CrossRefGoogle Scholar
  9. 9.
    Jansen, B.J., Spink, A., Koshman, S.: Web searcher interaction with the metasearch engine. J. Am. Soc. Inf. Sci. Technol. 58, 744–755 (2007)CrossRefGoogle Scholar
  10. 10.
    Kelly, D., Gyllstrom, K., Bailey, E.W.: A comparison of query and term suggestion features for interactive searching. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 371–378 (2009)Google Scholar
  11. 11.
    Marchionini, G., White, R., Belkin, N., Golovchinsky, G., Kelly, D., Pirolli, P., Schraefel, M.: Information Seeking Support Systems: An invitational workshop sponsored by the National Science Foundation (2008)Google Scholar
  12. 12.
    Mayr, P., Petras, V.: Cross-concordances: terminology mapping and its effectiveness for information retrieval. In: 74th IFLA World Library and Information Congress, Québec, Canada (2008)Google Scholar
  13. 13.
    Nettle, D.: Segmental inventory size, word length, and communicative efficiency. Linguistics 2, 359–367 (1995)Google Scholar
  14. 14.
    Petras, V.: How one word can make all the difference - using subject metadata for automatic query expansion and reformulation. In: Peters, C., Gey, F.C., Gonzalo, J., Müller, H., Jones, G.J.F., Kluck, M., Magnini, B., de Rijke, M., Giampiccolo, D. (eds.) CLEF 2005. LNCS, vol. 4022, pp. 21–23. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Plaunt, C., Norgard, B.A.: An association-based method for automatic indexing with a controlled vocabulary. J. Am. Soc. Inf. Sci. 49, 888–902 (1998)Google Scholar
  16. 16.
    Schatz, B.R., Johnson, E.H., Cochrane, P.A., Chen, H.: Interactive term suggestion for users of digital libraries: using subject thesauri and co-occurrence lists for information retrieval. In: Proceedings of the First ACM International Conference on Digital Libraries, pp. 126–133. ACM, New York (1996)CrossRefGoogle Scholar
  17. 17.
    Shiri, A., Revie, C.: Query expansion behavior within a thesaurus-enhanced search environment: A user-centered evaluation. J. Am. Soc. Inf. Sci. Technol. 57, 462–478 (2006)CrossRefGoogle Scholar
  18. 18.
    Vechtomova, O., Robertson, S., Jones, S.: Query Expansion with Long-Span Collocates. Inf. Retr. 6, 251–273 (2003)CrossRefGoogle Scholar
  19. 19.
    White, R.W., Bilenko, M., Cucerzan, S.: Studying the use of popular destinations to enhance web search interaction. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 159–166. ACM, New York (2007)Google Scholar
  20. 20.
    White, R.W., Marchionini, G.: Examining the effectiveness of real-time query expansion. Inf. Process. Manage. 43, 685–704 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.GESIS - Leibniz Institute for the Social SciencesBonnGermany

Personalised recommendations