Personalized Cross Ontological Framework for Secured Document Retrieval in the Cloud

Personalization is crucial in the prevailing internet scenario. An efficient information retrieval relies not only on personalization but also upon security. For this, a semantic association is needed for document retrieval, to further boost the search quality. In this paper, multiple ontologies are used, for mining the semantic associations. Multiple ontologies can share a variety of concepts and automatic merging is also feasible. With semantic association any input query is matched for finding relevant words. Semantic similarity measure is proposed to match the semantic association of query therewith of the documents, based on the query expansion technique. A user profiling model is constructed which is trained for personalization. This model ensures secure retrieval of the documents. A novel framework is proposed for providing secured document retrieval based on cross ontology mining. The results show good MAP and F-Measure rate of document retrieval when compared to the existing techniques.