Use cases of heterogeneous learning ontologies

Abstract

The emergence of new repositories of learning resources based on semantic technology raises the problem of ontologies interoperability. Learning resources are described by a set of metadata which may use different ontologies as referential. To handle the increasing number of heterogeneous ontologies it becomes necessary to develop automatic mapping approach. In this paper we propose to study technical solutions for ontologies handling through two use cases: (1) the comparison between a current learner profile and a target profile and (2) the search of learning resources among distributed repositories.