Ensemble based Machine Learning using Ontology Information Extraction for Information Retrieval

Abstract

The movement of internet technology enhanced the speed and accuracy of data retrieval over the internet. The retrieval of data over the internet needs some automatic process of information extraction and query retrieval. The information extraction gives the process of the predefined structure of the concept to a particular domain of knowledge. The process of information extraction proceeds in two steps one is pre-processing of data and post-processing of data. In preprocessing of data used the concept of the glowworm optimization algorithm. The glowworm algorithm is a family of kits a gives the better selection of information in constraints of similarity. The selection of similarity based on the process of lubrification. The optimization of glowworm removed the unwanted noise of data and filtered it. For the extraction of information used ensemble-based information extraction. The ensemble-based information extraction proceeds with constraints function that function is called mapper constraints. The mapper constraints map the process of ontology with guided domain ontology. The ensemble-based information extraction process used the concept of machine learning for the binding of process. The goals of this work are the development of an OBIE for the domain of different fields of data retrieval such as news agencies, hotel industries and sports. The proposed model combines with the use of ontology, POS and language processing tools and constraints-based mapper with domain ontology.