Process Knowledge Graphs (PKG): Towards unpacking and repacking AI applications

In the past years, a new generation of systems has emerged, which apply recent advances in generative Artificial Intelligence (AI) in combination with traditional technologies. Specifically, generative AI is being delegated tasks in natural language or vision understanding within complex hybrid architectures that also include databases, procedural code, and interfaces. Process Knowledge Graphs (PKG) have a long-standing tradition within symbolic AI research. On the one hand, PKGs can play an important role in describing complex, hybrid applications, thus opening the way for addressing fundamental challenges such as explaining and documenting such systems (unpacking). On the other hand, by organising complex processes in simpler building blocks, PKGs can potentially increase accuracy and control over such systems (repacking). In this position paper, we discuss opportunities and challenges of PGRs and their potential role towards a more robust and principled design of AI applications. • Generative AI is embedded in applications that are difficult to explain and document. • Process Knowledge Graphs can offer an abstraction over opaque AI systems (unpacking). • Prompt engineering fuels complex applications in rich, AI-powered workflows. • Knowledge Graphs can recombine atomic operations in a principled way (repacking).