Python-based Implementation of Ontology Modularity Tools

Supervisor: Jieying Chen (


Ontology modularity plays a crucial role in large-scale knowledge representation and management, enabling users to extract relevant sub-ontologies and facilitating efficient reasoning, updating, and maintenance. Despite its importance, there’s a noticeable gap in the market for high-speed, Python-based tools tailored to ontology modularity. This research aims to fill that gap by leveraging Python’s versatile ecosystem and designing tools that are not only accurate but also significantly faster.


  1. Survey existing ontology modularity tools, understand their computational complexities, and identify areas of potential improvement in terms of speed and efficiency.
  2. Outline the architecture for the Python-based modularity tool, ensuring a balance between modular design and computational efficiency.
  3. Research, design, and implement algorithms that are optimized for speed and are tailored specifically for ontology modularity tasks.
  4. Ensure seamless integration of the developed tool with popular Python libraries and frameworks, enhancing user experience and expandability.
  5. Compare the developed tool against existing ontology modularity solutions in terms of speed, efficiency, and accuracy.