Design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification in the healthcare industry

Abstract

Ontology generation is a process of relationship analysis and representation for multiple data categories using automatic or semi-automatic approaches. Thus, the main contribution of this paper is the design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification (BOGMAS) that can overcome existing issues. The BOGMAS model uses a semi-supervised approach for ontology generation from almost any structured or unstructured dataset. This model uses a combination of linear support vector machine, and extra trees stratifies for variance estimation, which makes the model highly efficient, and reduces redundant features from the output ontology. The generated ontology is represented using an incremental OWL (W3C Web Ontology Language) format, which assists in dynamically sizing the ontology depending on incoming data. The performance of the proposed BOGMAS model is evaluated in terms of precision and recall of representation, memory usage, computational complexity, and accuracy of attack detection. It is observed that the proposed model is highly efficient in terms of precision, recall and accuracy performance, but has incrementally higher computational complexity and delay of ontology formation when compared with existing approaches. Due to this incremental increase in delay, the proposed model is observed to be applicable for a wide variety of real-time scenarios, which include but are not limited to, medical ontology generation, sports ontology generation, and internet of things ontology generation with high-security levels.