Managing Uncertainty in Ontology Mapping in E-Learning Context

Abstract

Many mapping methods have been proposed in recent years, but every of them works well only for specific type of ontologies or in well-defined context. A grand number of mapping methods propose mappings, leading to inconsistency of resulting ontological network, and in such a way make it unusable in many reasoning-based applications. And most of automatically proposed mappings are uncertain in some sense. In this paper we make a short analysis of the approaches for modelling ontology mapping uncertainty and the ways for detecting logical errors, resulting from incorrect mappings. We discuss some important mapping, mapping evaluation and refinement strategies, well working in e-learning domain.