Automatic text indexing using complex identifiers
DOCPROCS '88 Proceedings of the ACM conference on Document processing systems
The effect of adding relevance information in a relevance feedback environment
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Reusing Learning Resources based on Semantic Web Technologies
ICALT '06 Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies
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Users look frequently for learning information from the web or from databases to include them to their resources, or to use them in a learning process. In order to satisfy these user's needs, we proposed here a model which aims at automatically annotating texts with semantic metadata: learning category of textual segments. 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.