A novel semantic approach in E-learning information retrieval system

Nowadays growing number of popularization in the World Wide Web promotes e-learning via web. During e-learning the users can easily share, reuse, and organize the knowledge. Using the search engine the e-learners search the web pages by set of keywords. But the pages which are unrelated for our tags come frequently are the major problem nowadays. There is always a semantic gab between searching the web pages and its representation. Ontology Based Text Mining (OBTM) with the help of human concept makes the search meaningfully and gives the relevance site first. Here e-learning with the help of several OBTM techniques such as Concept Weight Based Ontology (CWBO), NLP Based Text Mining (NBTM), Based on Data Quality (BDQ), Personalization (P), Question Answering System (QAS) and Rule Based Recommender System (RR) are analyzed. In this paper we proposed the ontology based information retrieval system using web ontology languages and analyzed the importance of handling concepts using tools Wordnet or Hownet. Here ontology is created after the preprocessing process with e-learning documents such as stop word removal, stemming, and whitespace removal and so on. We proved that the proposed ontology based NBTM information retrieval technique is efficient and effective in terms of precision and recall parameters.