Intelligent ontology based semantic information retrieval using feature selection and classification

Semantic information retrieval provides more relevant information to the user query by performing semantic analysis. In such a scenario, knowledge representation using ontology can provide effective semantic retrieval facility which is more efficient than representation using semantic networks and frames. The existing information retrieval systems have been developed to handle very large volume of data and information stored in text format. On the other hand, the information available in the current web based applications such as Facebook and twitter grow very fast and hence the existing information retrieval systems consume large amount of time for relevant information retrieval. Moreover, most of the existing search engines use syntactic approach for information retrieval and use page ranking algorithms to measure the relevancy score. However, such approach is not able to provide more accurate results in terms of relevancy. Therefore, a new semantic information retrieval system is proposed in this paper which uses feature selection and classification for enhancing the relevancy score which is performed in this work by proposing a new intelligent fuzzy rough set based feature selection algorithm and an intelligent ontology and Latent Dirichlet Allocation based semantic information retrieval algorithm. The main advantages of the proposed algorithms are the increase in relevancy, ability to handle big data and fast retrieval.