Accessing the Distributed Learner Profile in the Semantic Web

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Abstract

Adaptive e-learning systems need to get complete learner profile to make efficient personalization. However, learner profile is dispersed over multiple heterogeneous e-learning systems. Unfortunately, these e-learning systems are heterogeneous what makes difficult to get complete learner profile. By using semantic web technology, namely Topic Maps, we show how we perform integration of heterogeneous fragments of learner profile to get more complete one. However, this technology does not consider constraints and is not able to make reasoning. So we use, together with Topic Maps, Description Logics to represent constraints and make reasoning over integrated data.