Toward principles for the design of ontologies used for knowledge sharing
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Ontologies for Multi-Agent Systems in Manufacturing Domain
DEXA '02 Proceedings of the 13th International Workshop on Database and Expert Systems Applications
Understanding and predicting effort in software projects
Proceedings of the 25th International Conference on Software Engineering
Semantic integration: a survey of ontology-based approaches
ACM SIGMOD Record
AI Magazine - Special issue on semantic integration
ISSRE '05 Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering
The Challenges of Building Advanced Mechatronic Systems
FOSE '07 2007 Future of Software Engineering
Model-Driven Testing: Using the UML Testing Profile
Model-Driven Testing: Using the UML Testing Profile
Ontology-supported quality assurance for component-based systems configuration
Proceedings of the 6th international workshop on Software quality
SEAA '08 Proceedings of the 2008 34th Euromicro Conference Software Engineering and Advanced Applications
An analysis framework for ontology querying tools
Proceedings of the 9th International Conference on Semantic Systems
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The engineering of a complex production automation system involves experts from several backgrounds, such as mechanical, electrical, and software engineering. The production automation expert knowledge is embedded in their tools and data models, which are, unfortunately, insufficiently integrated across the expert disciplines, due to semantically heterogeneous data structures and terminologies. Traditional integration approaches to data integration using a common repository are limited as they require an agreement on a common data schema by all project stakeholders. This paper introduces the Engineering Knowledge Base EKB, a semantic-web-based framework, which supports the efficient integration of information originating from different expert domains without a complete common data schema. The authors evaluate the proposed approach with data from real-world use cases from the production automation domain on data exchange between tools and model checking across tools. Major results are that the EKB framework supports stronger semantic mapping mechanisms than a common repository and is more efficient if data definitions evolve frequently.