The SRIDoP System Using Semantic Metadata for Web Database Processing
Boutheina Smine, Rim Faiz, Jean-Pierre Desclés
Journal of Information Technology Review, Vol. 2, No. 3, 2011.
DOI: 
Link: http://www.dline.info/jitr/fulltext/v2n3/4.pdf
Abstract:
Searching learning information from the web or from databases is a user’s need to learn or to teach. In order to satisfy these user’s needs, we proposed here a model which aims at automatically feeding texts with semantic metadata. These metadata would allow us to search and extract learning information from texts indexed in that way. This model is build up from two parts: the first part consists on 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 which is able to find relevant learning objects for queries associated with semantic categories. To sort the results according to their relevance, we apply the Rocchio’s classification technique on the learning objects. We have implemented a system called SRIDoP, on the basis of the proposed model and we have verified its effectiveness.