Relevant learning objects extraction based on semantic annotation. (1st January 2013) - Smine, Boutheina; Faiz, Rim; Desclés, Jean-Pierre
We propose, in this paper, a model that extracts automatically learning objects as response to a user request. To do this, we proceed by automatically annotating texts with semantic metadata. These metadata will allow us to index and extract learning objects from texts. Thus, our model is composed of two principal parts: the first part consists of a semantic annotation of learning objects according to their semantic categories (definition, example, exercise, etc.). The second part uses automatic semantic annotation which is generated by the first part to create a semantic inverted index able to find relevant learning objects for queries associated with semantic categories. We add a secondary part to our model which sorts the results offered to the user according to their relevance. We have implemented a system called SRIDoP, on the basis of the proposed model and we have verified its effectiveness.