article · SoftwareX
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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1016/j.softx.2025.102333
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
