A light-weight dynamic ontology for Internet of Things using machine learning technique

Abstract Ensuring semantic interoperability in the future Internet of Things can be a challenging task due to their heterogeneous nature and increasing scale. Ontologies are widely used to achieve semantic interoperability among IoT applications and services. But, available ontologies are very complex, static or unable to fulfill the requirements of IoT. To address this concern, we proposed a light-weight dynamic ontology using only the most important concepts and clustering technique. It provides dynamic semantics automatically to include additional concepts using machine learning technique. Compared to the existing ontology, the proposed model reduces query response time and memory consumption to some extent.