Learning Objects Retrieval Algorithm Using Semantic Annotation and New Matching Score

Abstract

Learning objects retrieval is important for a variety of information needs and applications such as collection management, summary and analysis. Especially, retrieval of learning objects from the big collection of textual database has become a very active field. Since the learning information is stored in textual format in most of the time, the retrieval of learning objects from textual database faces two major challenges such as, large data handling, and effectiveness. If these two challenges are solved, the performance and its applicability will be improved significantly. Accordingly, the first challenges of large data processing will be handled through the semantic annotations method. In the semantic annotation method, the text document was converted to semantic annotation data using concept-based modeling. With the help of existing work, the large data will be converted to annotation object so that the matching process with the input query will be reduced and the complexity of handling big data will be also reduced. The second challenge of effectiveness will be solved using new matching score to obtain better retrieval effectiveness. Here, query will be matched with the annotated object using matching score that will be devised newly and it will be applied for learning object retrieval. The experimentation will be carried out utilizing the learning objects given in IEEE digital library and the performance of the proposed technique is evaluated using precision, recall and F-measure and also, comparative analysis will be performed to prove the better performance of the proposed technique. The analysis from the experimentations showed that the proposed approach has obtained a maximum precision of 0.98 and average precision of 0.95.