Automated conceptual model clustering: a relator-centric approach.

Area of Science:

• Computer Science
• Information Science
• Ontology Engineering

Background:

• Reference conceptual models are crucial for semantic interoperability in complex domains like finance and healthcare.
• Ensuring these models are cognitively tractable is essential for domain experts.
• Existing methods may not adequately address the complexity and semantic richness required.

Purpose of the Study:

• To propose a novel model clustering technique for ontology-driven conceptual models (ODCM).
• To enhance the cognitive tractability of complex conceptual models.
• To facilitate semantic interoperability tasks through improved model understanding.

Main Methods:

• Development of a formal notion of Relational Context to capture entity roles within reified relationships.
• Automated identification of Relational Contexts for conceptual model clusterization (modular breakdown).
• Implementation of computational support for the proposed technique.

Main Results:

• The proposed Relational Context approach effectively guides the automated modular breakdown of ODCM.
• Computational tools successfully automate the identification of Relational Contexts and model clustering.
• Empirical evaluation demonstrates the cognitive effectiveness of the approach.

Conclusions:

• The Relational Context-based clustering technique significantly improves the cognitive tractability of ODCM.
• Automated modular breakdown enhances the usability of conceptual models for domain experts.
• This approach offers a scalable solution for managing and understanding complex information systems.