OntologyGen: A smart software for automatic ontology generation from MongoDB using Formal Concept Analysis
Elmehdi Elguerraoui, Omar Boutkhoum, Mohamed Hanine, Waeal J. Obidallah · SoftwareX · 2025
OntologyGen is a web-based framework that automates OWL ontology generation from MongoDB databases, using Formal Concept Analysis (FCA). Built with Python and Django, It extracts a formal context from NoSQL data, builds concept lattices, and applies rule-based mappings to produce OWL ontologies. OntologyGen offers an interactive graphical interface that requires less user involvement, allows the user to extract semantic structures from schema-flexible data, and then builds OWL ontologies that can be used with other existing tools. By using two publicly available MongoDB datasets of varying complexity, the framework’s usability and efficacy were established, with a subsequent assessment of performance metrics including execution time, memory footprint, and ontology size. It was concluded that OntologyGen represents a considerable opportunity to reduce the difficulty of ontology engineering for data scientists and domain experts, while also providing scalability, interoperability, and extensibility beyond the current implementation with other NoSQL systems or possible future ontology learning extensions.