Abstract
As practical tools for disciplined multi-level modeling have begun to mature, the problem of supporting simple and efficient transformations to-and-from multi-level models to facilitate interoperability has assumed growing importance. The challenge is not only to support efficient transformations between multi-level models, but also between multi-level and two-level model content represented in traditional modeling infrastructures such as the UML and programming languages. Multi-level model content can already be accessed by traditional transformation languages such as ATL and QVT, but in a way that is blind to the ontological classification information they contain. In this paper, we present an approach for making rule-based transformation languages "multi-level aware" so that the semantics of ontological classification as well as linguistic classification can be exploited when writing transformations.
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