A Flexible Rule-Based Method for Interlinking, Integrating, and Enriching User Data

  • Erwin Leonardi
  • Fabian Abel
  • Dominikus Heckmann
  • Eelco Herder
  • Jan Hidders
  • Geert-Jan Houben
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6189)


Many Web applications provide personalized and adapted services and contents to their users. As these Web applications are becoming increasingly connected, a new interesting challenge in their engineering is to allow the Web applications to exchange, reuse, integrate, interlink, and enrich their data and user models, hence, to allow for user modeling and personalization across application boundaries. In this paper, we present the Grapple User Modeling Framework (GUMF) that facilitates the brokerage of user profile information and user model representations. We show how the existing GUMF is extended with a new method that is based on configurable derivation rules that guide a new knowledge deduction process. Using our method, it is possible not only to integrate data from GUMF dataspaces, but also to incorporate and reuse RDF data published as Linked Data on the Web. Therefore, we introduce the so-called Grapple Derivation Rule (GDR) language as well as the corresponding GDR Engine. Further, we showcase the extended GUMF in the context of a concrete project in the e-learning domain.


user modeling user data integration personalization semantic enrichment knowledge derivation 


  1. 1.
    Abel, F., Henze, N., Herder, E., Krause, D.: Interweaving Public Profile Data on the Web, Technical Report, L3S Research Center, Hannover, Germany (2010)Google Scholar
  2. 2.
    Broeskstra, J., Kampman, A.: SeRQL: A Second Generation RDF Query Language. In: SWAD-Europe Workshop on Semantic Web Storage and Retrieval, Vrije Universiteit, Amsterdam, Netherlands (2003)Google Scholar
  3. 3.
    Firan, C.S., Nejdl, W., Paiu, R.: The Benefit of Using Tag-based Profiles. In: Proc. of LA-WEB 2007, Washington, DC, USA. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  4. 4.
    Prud’hommeaux, E., Seaborne, A.: SPARQL Query Language for RDF. W3C Recommendation (January 2008),
  5. 5.
    Stewart, C., Celik, I., Cristea, A., Ashman, H.: Interoperability between aeh user models. In: Proc. of APS 2006 (2006)Google Scholar
  6. 6.
    Aroyo, L., Dolog, P., Houben, G., Kravcik, M., Naeve, A., Nilsson, M., Wild, F.: Interoperability in personalized adaptive learning. J. Educational Technology &Society 9(2), 4–18 (2006)Google Scholar
  7. 7.
    Berners-Lee, T.: Design Issues: Linked Data (2006),
  8. 8.
    Quilitz, B., Leser, U.: Querying Distributed RDF Data Sources with SPARQL. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 524–538. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Hartig, O., Bizer, C., Freytag, J.-C.: Executing SPARQL Queries over the Web of Linked Data. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 293–309. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Langegger, A., Wöß, W., Blöchl, M.: A semantic web middleware for virtual data integration on the web. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 493–507. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Abel, F., Heckmann, D., Herder, E., Hidders, J., Houben, G.-J., Krause, D., Leonardi, E., van der Sluijs, K.: A Framework for Flexible User Profile Mashups. In: The Proc. of the APWEB 2.0 2009 Workshop in conjunction UMAP 2009 (2009)Google Scholar
  12. 12.
    Langegger, A.: Virtual data integration on the web: novel methods for accessing heterogeneous and distributed data with rich semantics. In: Proc. of iiWAS’08 (2008)Google Scholar
  13. 13.
    Schenk, S., Staab, S.: Networked graphs: a declarative mechanism for sparql rules, sparql views and rdf data integration on the web. In: Proc. of WWW ’08 (2008)Google Scholar
  14. 14.
    Zemanek, J., Schenk, S., Svatek, V.: Optimizing sparql queriesover disparate rdf data sources through distributed semi-joins. In: ISWC 2008 Poster and Demo Session Proceedings. CEUR-WS (2008)Google Scholar
  15. 15.
    Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M.: SWRL: A Semantic Web Rule Language Combining OWL and RuleML,
  16. 16.
    McGuinness, D.L., van Harmelen, F. (eds.): OWL Web Ontology Language Overview, W3C Recommendation (February 2004),
  17. 17.
    OWL W3C: Working Group (eds.): OWL 2 Web Ontology Language Document Overview, W3C Recommendation (October 2009),
  18. 18.
    Rule Markup Language Initiative. Rule Markup Language (RuleML),
  19. 19.
    Kifer, M.: Rule Interchange Format: The Framework. In: Calvanese, D., Lausen, G. (eds.) RR 2008. LNCS, vol. 5341, pp. 1–11. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  20. 20.
    Heckmann, D., Schwartz, T., Brandherm, B., Schmitz, M., von Wilamowitz-Moellendorff, M.: GUMO - The General User Model Ontology. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, Springer, Heidelberg (2005)Google Scholar
  21. 21.
    Kuflik, T.: Semantically-Enhanced User Models Mediation: Research Agenda. In: Proc. of UbiqUM 2008 Workshop at IUI 2008, Gran Canaria, Spain (2008)Google Scholar
  22. 22.
    Finkel, J.R., Grenager, T., Manning, C.: Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. In: Proc. of ACL 2005 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Erwin Leonardi
    • 1
  • Fabian Abel
    • 2
  • Dominikus Heckmann
    • 3
  • Eelco Herder
    • 2
  • Jan Hidders
    • 1
  • Geert-Jan Houben
    • 1
  1. 1.Delft University of TechnologyDelftThe Netherlands
  2. 2.L3S Research CenterHannoverGermany
  3. 3.German Research Center for Artificial IntelligenceSaarbruckenGermany

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