01719nas a2200205   4500008004100000245007700041210006900118260000900187300001400196490000600210520103900216653002701255653002101282653002201303653002401325100001401349700001301363700002001376856011701396       2011                        eng d00aA semantic annotation model for indexing and retrieving learning objects0 asemantic annotation model for indexing and retrieving learning o  c2011  a159 - 1660 v93 aThe internet is an important part of our world. Offering a large and increasing amount of information, people can use it for learning, teaching, etc. Automatic tools for learning information retrieval based on semantic tags have not been effective yet. We propose here a model which aims at automatically annotating texts with semantic metadata. These metadata would allow us to index and extract learning objects from texts. This model is composed of two parts. While the first part consists of a semantic annotation of learning objects according to their categories (definition, example, exercise, etc.), the second one uses automatic semantic annotation. Generated by the first part, the latter aims at creating a semantic inverted index able to find relevant learning objects for queries. To sort the results according to their relevance, we apply the Rocchio's classification technique to the learning objects. We have implemented a system called SRIDoP, on the basis of the proposed model and we have verified its effectiveness.10aContextual exploration10aLearning objects10aRocchio algorithm10aSemantic annotation1 aSmine, B.1 aFaiz, R.1 aDesclés, J.-P.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-80053105137&partnerID=40&md5=a9c6f1a4944e56cbefac59e5ce025592