A comparative analysis of methodologies for database schema integration
ACM Computing Surveys (CSUR)
A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
The entity-relationship model—toward a unified view of data
ACM Transactions on Database Systems (TODS) - Special issue: papers from the international conference on very large data bases: September 22–24, 1975, Framingham, MA
Quantitative analysis of static models of processes
Journal of Systems and Software - Special issue on Evaluation and assessment in software engineering
Integrating relational database schemas using a standardized dictionary
Proceedings of the 2001 ACM symposium on Applied computing
Aris-Business Process Modeling
Aris-Business Process Modeling
A Relationship-Driven Framework for Model Merging
MISE '07 Proceedings of the International Workshop on Modeling in Software Engineering
Measuring similarity between semantic business process models
APCCM '07 Proceedings of the fourth Asia-Pacific conference on Comceptual modelling - Volume 67
User-friendly semantic annotation in business process modeling
WISE'07 Proceedings of the 2007 international conference on Web information systems engineering
Design science in information systems research
MIS Quarterly
A general approach to the generation of conceptual model transformations
CAiSE'05 Proceedings of the 17th international conference on Advanced Information Systems Engineering
Business Process Analysis and Optimization: Beyond Reengineering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Recommendation-based editor for business process modeling
Data & Knowledge Engineering
An eclipse plugin for improving the quality of UML conceptual schemas
ER'12 Proceedings of the 2012 international conference on Advances in Conceptual Modeling
A method for the definition and treatment of conceptual schema quality issues
ER'12 Proceedings of the 31st international conference on Conceptual Modeling
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A precondition for the appropriate analysis of conceptual models is not only their syntactic correctness but also their semantic comparability. Assuring comparability is challenging especially when models are developed by different persons. Empirical studies show that such models can vary heavily, especially in model element naming, even if they express the same issue. In contrast to most ontology-driven approaches proposing the resolution of these differences ex-post, we introduce an approach that avoids naming differences in conceptual models already during modeling. Therefore we formalize naming conventions combining domain thesauri and phrase structures based on a lin-guistic grammar. This allows for guiding modelers automatically during the modeling process using standardized labels for model elements. Our approach is generic, making it applicable for any modeling language.