AIOIML: Automatic Integration of Ontologies for IoT Domain Using Hybridized Machine Learning Techniques

Abstract

There is a need for Ontology modelling and automatic generation of Ontologies in order to assimilate knowledge World Wide Web knowledge as a strategic model. Ontologies are the best knowledge descriptor model as they have some amount of human cognition associated with them because either humans are major contributors when they are generated manually or are indirect contributors when they are semi automatically generated. Internet of Things is a domain which has strategically evolved in the last few years, and there is a need for integrating several facets of Internet of Things Ontology. In this paper a strategic scheme for Internet of Things Ontology integration for Internet of Things domain with different perspective are proposed wherein the dataset are subjected to tag generation which is further classified using the AdaBoost classifier which are aligned with the random core classes of the existing variational Ontologies in the Internet of Things domain using Shannon’s entropy and the pointwise mutual information measure with differential step deviation measure. Which yields average precision and recall of 96.83 and 97.95 respectively.