Open Access
ARTICLE
Generic Role-Based Model Management And Ontology-Driven Semantic Integration In Software Engineering Ecosystems
Issue Vol. 3 No. 01 (2026): VOLUME 03 ISSUE 01 --- Section Articles
Abstract
The increasing structural and semantic complexity of contemporary software-intensive systems has placed unprecedented pressure on traditional model management and integration approaches. As software systems evolve into distributed, service-oriented, and semantically enriched ecosystems, the need for robust mechanisms that can coordinate heterogeneous models, roles, and ontologies has become a central research challenge. This article develops an extensive theoretical and analytical investigation into the convergence of role-based model management and ontology-driven semantic frameworks, positioning generic role-based metamodels as foundational infrastructures for scalable model interoperability. Drawing on seminal work in generic role modeling for model management (Kensche et al., 2007), the study situates role-based abstractions within broader traditions of model-driven architecture, semantic web services, ontology learning, and software architecture description languages. Through sustained theoretical elaboration, the article demonstrates how role-based metamodels function as mediating layers that reconcile syntactic heterogeneity with semantic alignment, thereby enabling dynamic, context-aware model coordination across organizational and technological boundaries.
The methodological orientation of the study is interpretive and analytical, grounded in systematic conceptual synthesis rather than empirical experimentation. By critically examining established ontological approaches to component matching, web service orchestration, and semantic extraction, the article reveals persistent gaps in existing frameworks, particularly regarding flexibility, reuse, and role variability. The results of this analysis articulate a coherent interpretive model in which role-based metamodels serve as semantic pivot points, allowing ontologies to be operationalized within model-driven processes without collapsing into rigid structural schemas. The discussion advances a deep theoretical dialogue between competing scholarly positions, addressing critiques related to over-abstraction, ontological rigidity, and governance complexity. Ultimately, the article argues that generic role-based model management constitutes not merely a technical solution but an epistemic strategy for managing meaning, responsibility, and coordination in complex software ecosystems. The conclusions outline implications for future research in semantic interoperability, collaborative modeling, and adaptive software architecture, emphasizing the necessity of integrating role theory, ontology engineering, and model management into a unified conceptual framework.
Keywords
References
1. An Ontology for Software Component Matching. Pahl, C. In Proceedings of Fundamental Approaches to Software Engineering FASE’2003. Springer-Verlag, 2003.
2. A Categorization of Collaborative Business Process Modeling Techniques. Roser, S.; Bauer, B. IEEE, 2005.
3. Ontology Support for Web Service Processes. Pahl, C.; Casey, M. ACM Press, 2003.
4. GeRoMe: A Generic Role Based Metamodel for Model Management. Kensche, D.; Quix, C.; Chatti, M. A.; Jarke, M. Journal on Data Semantics, 2007.
5. A Classification and Comparison Framework for Software Architecture Description Languages. Medvidovic, N.; Taylor, R. N. Springer-Verlag, 1997.
6. Semantic Web Services. Payne, T.; Lassila, O. IEEE Intelligent Systems, 2004.
7. Model Driven Architecture (MDA). Object Management Group, 2001.
8. A Survey of Ontology Learning Methods and Techniques. Gómez-Pérez, A.; Manzano-Macho, D. OntoWeb Deliverable, 2003.
9. A Survey of Ontology Learning Techniques and Applications. Asim, M. N.; Wasim, M.; Khan, M. U. G.; Mahmood, W.; Abbasi, H. M. Database, 2018.
10. Ontology of Folksonomy: A New Modelling Method. Echarte, F.; Astrain, J. J.; Córdoba, A.; Villadangos, J. E. SAAKM, 2007.
11. Semi-automatic Terminology Ontology Learning Based on Topic Modeling. Rani, M.; Dhar, A. K.; Vyas, O. Engineering Applications of Artificial Intelligence, 2017.
12. An Ontology Enrichment Approach by Using DBpedia. Booshehri, M.; Luksch, P. ACM Press, 2015.
13. A Conceptual Comparison of WSMO and OWL-S. Lara, R.; Roman, D.; Polleres, A.; Fensel, D. Springer-Verlag, 2004.
14. Mental Mechanisms. Bechtel, W. Routledge, 2008.
15. Discovering Complexity. Bechtel, W.; Richardson, R. C. Princeton University Press, 1993.
16. Usage Scenarios and Goals for Ontology Definition Metamodel. Object Management Group, 2004.
17. An Automatic Semantic Extraction Method for Web Data Interchange. Yao, Y.; Liu, H.; Yi, J.; Chen, H.; Zhao, X.; Ma, X. IEEE, 2014.
18. Semi-automatic Framework for Generating RDF Dataset from Open Data. Krataithong, P.; Buranarach, M.; Hongwarittorrn, N.; Supnithi, T. Springer, 2016.
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