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A Rule-Based Adaption Model for Ontology-Based Personalization

  • Thanh Tran
  • Philipp Cimiano
  • Anupriya Ankolekar
Part of the Studies in Computational Intelligence book series (SCI, volume 93)

Summary

Various adaptive hypermedia systems have been proposed to alleviate information overload on the Web by personalising the delivery of information and resources to the user. These systems have however been afflicted with difficulties in the acquisition of an accurate user model, a limited degree of customization offered to the user as well as general lack of user control on and transparency of the systems’ adaptive behavior. In this chapter, we argue that the use of rules on top on ontologies can enable adaptive functionality that is transparent and controllable for users. To this end, we present ODAS, a domain ontology for adaptive hypermedia systems, and a model for the specification of ODAS-based adaptation rules. We demonstrate the use of this model by showing how it can be instantiated within a knowledge portal to arrive at rules that exploit ODAS semantics to perform meaningful personalization.

Keywords

Domain Ontology Application Interaction Adaptation Rule Content Unit Proactive Personalization 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Thanh Tran
    • 1
  • Philipp Cimiano
    • 1
  • Anupriya Ankolekar
    • 1
  1. 1.Institute AIFBUniversity of KarlsruheGermany

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