Approach based on fuzzy ontology for situation identification in situation-aware ubiquitous learning environment

Situation identification has become a major issue for situation-aware ubiquitous learning environments. The identification process aims to infer learner's situation by aggregating detected context information pieces. Most recent situation identification approaches are basically focused on crisp ontological modelling and reasoning. Given the fact that crisp ontology is not able to deal with context information imperfection, a new approach for situation identification based on fuzzy ontology is proposed in this work. The proposition aims to evaluate for any observed runtime situation a certainty degree relative to the recognition of a typical u-learning situation, known as situation pattern. The pattern relative to the highest certainty degree is triggered as the most appropriate pattern to the observed learning situation. Experimental results are given to show the applicability of the proposed solution for u-learning situation identification under imperfection and to show to what extend fuzzy ontology out performs crisp one.