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The Adaptive Web General Model And the Use of Semantic Web Technologies

The Adaptive Web General Model And the Use of Semantic Web Technologies. Martin Bal ík KEG Seminar , V ŠE 29. November 2007. Overview of the presentation. The Adaptive Web Methods and Techniques of A daptation Existing Web Models Disadvantages of Existing Approaches

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The Adaptive Web General Model And the Use of Semantic Web Technologies

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  1. The Adaptive WebGeneral Model And the Use of Semantic Web Technologies Martin Balík KEG Seminar, VŠE 29. November 2007

  2. Overview of the presentation • The Adaptive Web • Methods and Techniques of Adaptation • Existing Web Models • Disadvantages of Existing Approaches • The Aim of our Research • GAM Model – Initial Reference for our Extension • Our General Adaptivity Model • Application in theFieldof E-learning • Our Experimental E-learning project • Conclusions and Future Work

  3. TheAdaptive Web • User adaptivesystems • User model • AdaptationEffect • Applictation fields of adaptation • E-learning • Community websites • Online information systems • E-commerce

  4. MethodsandTechniquesofAdaptation • Content level adaptation • Additional explanations • Prerequisite explanations • Comparative explanations • Sorting explanations • Link level adaptation • Direct guidance • Adaptive sorting • Adaptive hiding • Links annotation

  5. Existingmodelsofadaptive web • AHAM – AdaptiveHypermediaApplication Model • Munich Reference Model • FOHM – Fundamental Open Hypermedia Model • GAHM – GoldsmithsAdaptiveHypermedia Model • GAM – Generic Adaptivity Model

  6. Disadvantagesofexistingapproaches • No interchangeability of data • Content cannot be reused • Mostly ad-hoc solution • Personalization on different levels of approach • Different formats of implementation • Adaptive specification is not suitable for general use

  7. TheAimofourResearch • Specification of an appropriate formal approach to the general conceptual meta-model design • General meta-model formalization using a suitable description of adaptive behaviour • Carrying out several experiments and verifying the resulting adaptation system

  8. GAM Model – TheInitial Reference forourextension Push adaptation • Updating of the user model Pull adaptation • Querying the user model Brusilovsky, 1996 De Vrieze et al., 2006

  9. OurGeneral Adaptivity Model • Ability of the communication between distributed systems • User model → ontology understandable by various systems • Multidimensional matrix • Each layer contains ontological representation of specific kind of knowledge • Ontologiesare application independent, modular and layered • The type of the adaptation should be also adapted • Modelling user groups, collaboration • New methods of obtaining information • User feedback is necessary

  10. Semantic Description • Information should be understandable • not only to human users • but also to the computers which prepare data for the users • Ontologies play an important role in the Semantic Web • Provide a common shared model • To represent a domain • To reason about the objects in the domain • In our model we use ontology to represent all data sources • User model, Adapted content, Adaptation rules • Semantic annotation will make the data interchange more easy

  11. Application in theFieldof E-learning • E-learning is • typical application area of adaptive systems • optimal environment for testing purposes, especially at the university • E-leaning takes advantage of personalization to the user • Learning material is suitable to the users knowledge • User isn’t disturbed by unimportant parts • But it is very difficult to create the learning material without reusing already created learning objects

  12. Our Experimental E-learning project • Development of the ontology – Protégé • Web-based tool • Java language • Jena Semantic Framework • RDF2GO ? • RDF Reactor ? • Model-view-controllerarchitecture

  13. Our Experimental E-learning project • Ontology consists of two main parts • Course materials • This includes lecture slides, learning objects used to compose the personalized webpage and tasks for students • Data about progress of students in the course • Test scores, evidence of course attendance etc.

  14. Conclusions and Future Work • We have • analyzed the needs of general purpose adaptive systems • proposed an adaptation model • partially implemented a testing system and ontology • In the future we will • extend the testing system • perform experiments to verify our proposals • formulate a formal description of the adaptive system

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