Automating Semantic Annotation to Enable Learning Content Adaptation

  • Jelena Jovanović
  • Dragan Gašević
  • Vladan Devedžić
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4018)


This paper presents an approach to automatic annotation of learning objects’ (LOs) content units that can be later assembled into new LOs personalized to the users’ knowledge, preferences, and learning styles. Relying on a LO content structure ontology and some simple content-mining algorithms and heuristics, we manage to rather successfully determine the values of metadata elements aimed at annotating content units. Specifically, in this paper we present the specificities of generating metadata that describe the subject (based on a domain ontology) and the pedagogical role (based on an ontology of pedagogical roles) of a content unit. To test our approach we developed TANGRAM, an adaptive web-based educational environment for the domain of Intelligent Information system that enables on-the-fly assembly of personalized learning content out of existing content units.


Learning Style Domain Ontology Domain Concept Automatic Annotation Semantic Annotation 
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.
    Brusilovsky, P.: Methods and Techniques of Adaptive Hypermedia. In: Adaptive Hypertext and Hypermedia, pp. 1–43. Kluwer Academic Publishers, The Netherlands (1998)Google Scholar
  2. 2.
    Verbert, K., Klerkx, J., Meire, M., Najjar, J., Duval, E.: Towards a Global Component Architecture for Learning Objects: an Ontology Based Approach. In: Meersman, R., Tari, Z., Corsaro, A. (eds.) OTM-WS 2004. LNCS, vol. 3292, pp. 713–722. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Jovanović, J., Gašević, D., Devedžić, V.: Ontology-based Automatic Annotation of Learning Content. Int’l J. on Sem. Web and Inf. Sys. 2(2) (2006) (forthcoming)Google Scholar
  4. 4.
    Verbert, K., Jovanović, J., Gašević, D., Duval, E.: Repurposing Learning Object Components. In: Proc. of the OTM 2005 Worksh. on Ontologies, Semantics and E-learning, Agia Napa, Cyprus, pp. 1169–1178 (2005)Google Scholar
  5. 5.
    Liu, B., Chin, C.W., Ng, H.T.: Mining Topic-Specific Concepts and Definitions on the Web. In: Proc. of the 12th Int’l WWW Conf., Budapest, Hungary, pp. 251–260 (2003)Google Scholar
  6. 6.
    Dolog, P., Nejdl, W.: Challenges and Benefits of the Semantic Web for User Modeling. In: Proc. of AH 2003 Worksh. at 12th Int’l WWW Conf., Budapest, Hungary (May 2003)Google Scholar
  7. 7.
    Keenoy, K., Levene, M., Peterson, D.: Personalisation and Trails in Self e-Learning Networks, SeLeNe Working Package 4 Deliverable 4.2. (2003) (Online), Available at:
  8. 8.
    Felder, R., Silverman, L.: Learning and Teaching Styles In Engineering Education. Journal of Engineering Education 78(7), 674–681 (1988)Google Scholar
  9. 9.
    Jovanović, J., Gašević, D., Devedžić, V.: Dynamic Assembly of Personalized Learning Content on the Semantic Web. In: 3rd European Semantic Web Conf., Budva, Serbia & Montenegro (2006) (submitted)Google Scholar
  10. 10.
    De Bra, P., Aroyo, L., Cristea, A.: Adaptive Web-based Educational Hypermedia. In: Web Dynamics, Adaptive to Change in Content, Size, Topology and Use, pp. 387–410 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jelena Jovanović
    • 1
  • Dragan Gašević
    • 2
  • Vladan Devedžić
    • 1
  1. 1.FON, School of Business AdministrationUniversity of BelgradeSerbia and Montenegro
  2. 2.School of Interactive arts and TechnologySimon Fraser University SurreyCanada

Personalised recommendations