Ontology-based soft computing and machine learning model for efficient retrieval

Abstract

Unstructured and unorganized data always degrade the performance of search techniques and produce irrelevant results in response to the query as well as decrease the speed of retrieval results. Ontology in Semantic Web (SW) provides an adequate solution to represent the knowledge, because of its backbone knowledge of an application or domain. But, a domain ontology has three basic problems while retrieving useful knowledge from a domain ontology-structuring/arrangement, unnecessary knowledge reduction, selection and extraction, and speeding up the retrieval process. To handle such a problem, the MLK-rBO model is proposed that works for four different phases-clustering, knowledge discovery, building a probabilistic network, and model evaluation using the ensemble approach of three different techniques namely clustering, rough set, and Bayesian network (BN). Finally, the proposed model is tested with statistical parameters and compared with other models namely DT, RF, and SVM to check the effectiveness of MLK-rBO. By analyzing the experimental results, we observed that the MLK-rBO gives better accuracy (98.36 %) than the available models.