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 arch...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Web Semantics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1570826824000325 |
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| Summary: | 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. |
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| ISSN: | 1570-8268 |