An Automatic Generation of Domain Ontologies Based on an MDA Approach to Support Big Data Analytics

Abstract

This chapter proposes a new methodology for the automatic generation of domain ontologies to support big data analytics. This method ensures the recommendations of the MDA approach by transforming UML class diagrams to domain ontologies in PSM level through ODM, which is an OMG standard for ontology modeling. In this work, the authors have focused on the model-driven architecture approach as the best solution for representing and generating ontology artifacts in an intuitive way using the UML graphical syntax. The creation of domain ontologies will form the basis for application developers to target business professional context; however, the future of big data will depend on the use of technologies to model ontologies. With that said, this work supports the combination of ontologies and big data approaches as the most efficient way to store, extract, and analyze data. It is shown using the theoretical approach and concrete results obtained after applying the proposed process to an e-learning domain ontology.

Knowledge-Based System for Crop Pests and Diseases Recognition

“…It is possible to distinguish three main categories: ontology learning, ontology population (a.k.a., ontology instantiation), and ontology evolution (a.k.a., ontology enrichment). Ontology learning involves the extraction of new concepts, relations, attributes, and axioms [75,76]. Due to this processing, the terminological component of ontologies (TBox) is modified.…”