Metamodeling is a widely applied technique in the field of graphical languages to create highly configurable modeling environments. These environments support the rapid development of domain-specific modeling languages (DSMLs). Design patterns are efficient solutions for recurring problems. With the proliferation of DSMLs, there is a need for domain-specific design patterns to offer solutions to problems recurring in different domains. The aim of this paper is to provide theoretical and practical foundations to support domain-specific model patterns in metamodeling environments. In order to support the treatment of premature model parts, we weaken the instantiation relationship. We provide constructs relaxing the instantiation rules, and we show that these constructs are appropriate and sufficient to express patterns. We provide the necessary modifications in metamodeling tools for supporting patterns. With the contributed results, a well-founded domain-specific model pattern support can be realized in metamodeling tools.

In previous publications we have reported

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The increasing amount and variety of domain knowledge and the availability of increasingly large quantities of electronic literature requires new types of support for the development of complex knowledge models. In previous publications we proposed the application of so called Annotated Bayesian Networks (ABN), textually enriched probabilistic domain models, which help knowledge engineers and medical experts to find and organize the information necessary in model building. In this paper we describe an information retrieval language in which the formalized domain knowledge and the attached textual information can be accessed in an integrated fashion and can be used to define various retrieval schemes and relevance measures. This language, on one hand, provides maximum flexibility for knowledge engineers to exploit the available annotated domain model as contextual information. On the other hand, it allows the definition of complex, high-level queries, in which the contextual use of the annotated domain model can be optimized for clinical situations. We compare the performance of the standard and the proposed query language in the ovarian cancer domain.