RDB2OWL: AN IMPROVED METHOD FOR CONVERTING RELATIONAL DATABASES INTO OWL by Phạm Thị Thu Thúy

“…One of the biggest advantages of the Semantic web is to describe data with a well-defined meaning and link between data by using the OWL (Web Ontology Language). Today most data are stored in relational databases. …”
Get full text

Ontological analysis in the problems of container applications threat modelling by A. I. Brazhuk, E. V. Olizarovich

“…Methods of ontological modelling and knowledge management are used in the work. The Web Ontology Language is used to represent knowledge; automatic reasoning based on description logics is used for threat modelling.Results. …”
Get full text

Building a Knowledge Base of Bridge Maintenance Using Knowledge Graph by Yang Zhang, Jia Liu, Kepeng Hou

“…Firstly, a bridge maintenance domain ontology (BMDO) is coded with Protégé and represented in Web Ontology Language. Secondly, rule reasoning and ontology reasoning are implemented on the BMDO in Protégé in order to automatically complete missing relations or attribute values. …”
Get full text

τJOWL: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data by Zouhaier Brahmia, Fabio Grandi, Rafik Bouaziz

“…Nowadays, ontologies, which are defined under the OWL 2 Web Ontology Language (OWL 2), are being used in several fields like artificial intelligence, knowledge engineering, and Semantic Web environments to access data, answer queries, or infer new knowledge. …”
Get full text

τSQWRL: A TSQL2-Like Query Language for Temporal Ontologies Generated from JSON Big Data by Zouhaier Brahmia, Fabio Grandi, Rafik Bouaziz

“…In a previous work, we have proposed an approach named τJOWL (temporal OWL 2 from temporal JSON, where OWL 2 stands for "OWL 2 Web Ontology Language" and JSON stands for "JavaScript Object Notation" ). τJOWL allows (1) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (2) to manage its incremental maintenance accommodating their evolution, in a temporal and multi-schema-version environment. …”
Get full text

An Ontology for Digital Medicine Outcomes: Development of the Digital Medicine Outcomes Value Set (DOVeS) by Benjamin Rosner, Matthew Horridge, Guillen Austria, Tiffany Lee, Andrew Auerbach

“…MethodsFrom a starting point of a 4-generation–deep hierarchical taxonomy developed by ADviCE, we developed DOVeS using the Web Ontology Language through the open-source ontology editor Protégé, and data from 185 vendors who had submitted structured product information to ADviCE. …”
Get full text