Augmented Ontology-Based Information Retrieval System with External Open Source Resources

As the number of online students has been growing significantly for the last couple of years, generic Web Information Retrieval methods have either maintained an emphasis on serving the general population, or have been lagging in integrating the power of adaptive semantics and personalization via knowledge discovery into real-life working E-learning applications. This paper is devoted to the implementation of an augmented ontology-based information retrieval system with external open-source resources from MIT OpenCourseWare that uses Protégé to design and build the structure of HyperManyMedia ontology as means of retrieving documents in two languages, English and Spanish. In addition, this paper deals with clustering the augmented documents, the outcome of the Clustering Analysis is added to the domain ontology as additional leaves under the “SubSubSubconcept = lecture”. From each cluster, we extract the Top
_n
keywords (descriptive terms), then we modify the ontology accordingly by adding the cluster's terms as semantic terms under the “SubSubSubconcept = lecture” to which these documents belong. This research is implemented and evaluated on a real platform HyperManyMedia at Western Kentucky University.