Social Information Retrieval Based on Semantic Annotation and Hashing upon the Multiple Ontologies

Ontology is the best way for representing the useful information. In this paper, we have planned to develop a model which utilizes multiple ontologies. From those ontologies, based on the mutual information among the concepts the taxonomy is constructed, then the relationship among the concepts is calculated. Thereby the useful information is extracted. There is multiple numbers of ontologies available through the web. But there are various issues to be faced while sharing and reusing the existing ontologies. To resolve the ambiguity which exists, when comparing two concepts are semantically similar, but physically different, an approach is proposed here to index and retrieve the documents from two different ontologies. The ontologies used are WordNet and SWETO ontology. The results are compared based on semantic annotation based on RMS and hashing between the cross ontologies using Rabin Karp fingerprinting algorithm. Also the datasets are trained to yield better results.