Robust Query Processing for Personalized Information Access on the Semantic Web

  • Peter Dolog
  • Heiner Stuckenschmidt
  • Holger Wache
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4027)


Research in Cooperative Query answering is triggered by the observation that users are often not able to correctly formulate queries to databases that return the intended result. Due to a lack of knowledge of the contents and the structure of a database, users will often only be able to provide very broad queries. Existing methods for automatically refining such queries based on user profiles often overshoot the target resulting in queries that do not return any answer. In this paper, we investigate methods for automatically relaxing such over-constraint queries based on domain knowledge and user preferences. We describe a framework for information access that combines query refinement and relaxation in order to provide robust, personalized access to heterogeneous RDF data as well as an implementation in terms of rewriting rules and explain its application in the context of e-learning systems.


User Preference Domain Preference Relaxation Strategy Triple Pattern Query Answering 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baader, F., Nipkow, T.: Term rewriting and all that. Cambridge University Press, Cambridge (1998)Google Scholar
  2. 2.
    Bruno, N., Gravano, L., Marian, A.: Evaluating top-k queries over web-accessible databases. In: Proceedings of the 18th International Conference on Data Engineering, p. 369. IEEE Computer Society Press, Los Alamitos (2002)CrossRefGoogle Scholar
  3. 3.
    Ceri, S.: A declarative approach to active databases. In: Golshani, F. (ed.) ICDE, pp. 452–456. IEEE Computer Society, Los Alamitos (1992)Google Scholar
  4. 4.
    Dolog, P., Henze, N., Nejdl, W., Sintek, M.: Personalization in distributed e-learning environments. In: Proc. of WWW 2004 — The Thirteen International World Wide Web Conference. ACM Press, New York (2004)Google Scholar
  5. 5.
    Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. In: PODS 2001: Proceedings of the twentieth ACM SIGMOD- SIGACT-SIGART symposium on Principles of database systems, pp. 102–113. ACM Press, New York (2001)CrossRefGoogle Scholar
  6. 6.
    Gaasterland, T., Godfrey, P., Minker, J.: An overview of cooperative answering. Journal of Intelligent Information Systems 1(2), 123–157 (1992)CrossRefGoogle Scholar
  7. 7.
    Güntzer, U., Balke, W.-T., Kießling, W.: Optimizing multi-feature queries for image databases. In: El Abbadi, A., Brodie, M.L., Chakravarthy, S., Dayal, U., Kamel, N., Schlageter, G., Whang, K.-Y. (eds.) VLDB 2000, Proceedings of 26th International Conference on Very Large Data Bases, Cairo, Egypt, September 10-14, 2000, pp. 419–428. Morgan Kaufmann, San Francisco (2000)Google Scholar
  8. 8.
    Hayes, P.: Rdf semantics. Recommendation, W3C (2004)Google Scholar
  9. 9.
    Kießling, W., Köstler, G.: Preference SOL- design, implementation, experiences. In: Proceedings of 28th International Conference on Very Large Data Bases (VLDB 2002), pp. 990–1001 (2002) Google Scholar
  10. 10.
    Lacroix, M., Lavency, P.: Preferences; putting more knowledge into queries. In: Stocker, P.M., Kent, W., Hammersley, P. (eds.) Proceedings of 13th International Conference on Very Large Data Bases (VLDB 1987), pp. 217–225. Morgan Kaufmann, San Francisco (1987)Google Scholar
  11. 11.
    Motro, A.: Flexx: A tolerant and cooperative user interface to database. IEEE Transactions on Knowledge and Data Engineering 2(2), 231–245 (1990)CrossRefGoogle Scholar
  12. 12.
    Papamarkos, G., Poulovassilis, A., Wood, P.T.: Event-condition-action rule languages for the semantic web. In: Cruz, I.F., Kashyap, V., Decker, S., Eckstein, R. (eds.) SWDB, pp. 309–327 (2003)Google Scholar
  13. 13.
    Paton, N.: Active Rules in Database Systems. Springer, Heidelberg (1999)zbMATHCrossRefGoogle Scholar
  14. 14.
    Stojanovic, N.: On analysing query ambiguity for query refinement: The librarian agent approach. In: Song, I.-Y., Liddle, S.W., Ling, T.-W., Scheuermann, P. (eds.) ER 2003. LNCS, vol. 2813, pp. 490–505. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  15. 15.
    Stuckenschmidt, H.: Similarity-based query caching. In: Christiansen, H., Hacid, M.-S., Andreasen, T., Larsen, H.L. (eds.) FQAS 2004. LNCS, vol. 3055, pp. 295–306. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Stuckenschmidt, H., van Harmelen, F.: Approximating terminological queries. In: Andreasen, T., Motro, A., Christiansen, H., Larsen, H.L. (eds.) FQAS 2002. LNCS, vol. 2522. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  17. 17.
    Stuckenschmidt, H., de Waard, A., Bhogal, R., Fluit, C., Kampman, A., van Buel, J., van Mulligen, E., Broekstra, J., Crowlesmith, I., van Harmelen, F., Scerri, T.: Exploring large document repositories with rdf technology - the dope project. IEEE Intelligent Systems (to appear, 2004)Google Scholar
  18. 18.
    Wache, H., Groot, P., Stuckenschmit, H.: Scalable instance retrieval for the semantic web by approximation. In: Ngu, A.H.H., Kitsuregawa, M., Neuhold, E.J., Chung, J.-Y., Sheng, Q.Z. (eds.) WISE 2005. LNCS, vol. 3806. Springer, Heidelberg (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Peter Dolog
    • 1
  • Heiner Stuckenschmidt
    • 2
  • Holger Wache
    • 3
  1. 1.L3S Research CenterHannoverGermany
  2. 2.Universität MannheimGermany
  3. 3.Vrije UniversiteitAmsterdamThe Netherlands

Personalised recommendations