Understanding ontological engineering
Communications of the ACM - Supporting community and building social capital
Software Engineering with Agents: Pitfalls and Pratfalls
IEEE Internet Computing
The Semantic Web: The Roles of XML and RDF
IEEE Internet Computing
Contributing to Eclipse: Principles, Patterns, and Plugins
Contributing to Eclipse: Principles, Patterns, and Plugins
Unified Modeling Language Reference Manual, The (2nd Edition)
Unified Modeling Language Reference Manual, The (2nd Edition)
Expert Systems with Applications: An International Journal
Model-driven ontology engineering
Journal on Data Semantics VII
A framework for building intelligent manufacturing systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In the past few years, software engineering has witnessed two major shifts: model-driven engineering has entered the mainstream, and some leading development tools have become open and extensible. AI has provided new ideas to software engineering, but most of its gems stayed buried in the laboratories, available only to a limited number of AI practitioners. Should AI tools be integrated into mainstream tools? And can this be done? We think it is feasible and that both communities can benefit from this integration. In fact, some efforts in this direction have already taken place, both from major industrial standardization bodies such as OMG and from academic laboratories. The example presented here should inspire readers to think about integrating their work with mainstream tools.