  Patterns describe proven solutions for recurring problems. Typically,
patterns in a particular domain are interrelated and organized in pattern
languages. As real-world problems often require patterns of multiple domains,
different pattern languages have to be considered to address these problems.
However, cross-domain knowledge about how patterns of different languages
relate to each other is either hidden in individual pattern descriptions or not
documented at all. This makes it difficult to identify relevant patterns across
pattern languages. Therefore, we introduce a concept and tooling that enables
to capture patterns and their relations across pattern languages for a
particular problem context.
