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Automated conceptual model clustering: a relator-centric approach.

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Summary

This study introduces a novel model clustering technique for ontology-driven conceptual models (ODCM). It enhances cognitive tractability by using Relational Contexts for automated modular breakdown, improving understanding of complex domains.

Keywords:
Complexity management in conceptual modelingConceptual model clusteringOntoUMLOntology-driven conceptual modeling

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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.