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 |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1570826824000325 |
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| _version_ | 1841545599421251584 |
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| author | Enrico Daga |
| author_facet | Enrico Daga |
| author_sort | Enrico Daga |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a332f1bcd58943e1886d94bb159a68b2 |
| institution | Kabale University |
| issn | 1570-8268 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Web Semantics |
| spelling | doaj-art-a332f1bcd58943e1886d94bb159a68b22025-01-12T05:24:30ZengElsevierWeb Semantics1570-82682025-01-0184100846Process Knowledge Graphs (PKG): Towards unpacking and repacking AI applicationsEnrico Daga0Knowledge Media Institute, The Open University, Walton Hall, Milton Keynes, MK7 6AA, United KingdomIn 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.http://www.sciencedirect.com/science/article/pii/S1570826824000325Knowledge graphsPrompt engineeringData science pipelinesData pipelines documentationData pipelines design |
| spellingShingle | Enrico Daga Process Knowledge Graphs (PKG): Towards unpacking and repacking AI applications Web Semantics Knowledge graphs Prompt engineering Data science pipelines Data pipelines documentation Data pipelines design |
| title | Process Knowledge Graphs (PKG): Towards unpacking and repacking AI applications |
| title_full | Process Knowledge Graphs (PKG): Towards unpacking and repacking AI applications |
| title_fullStr | Process Knowledge Graphs (PKG): Towards unpacking and repacking AI applications |
| title_full_unstemmed | Process Knowledge Graphs (PKG): Towards unpacking and repacking AI applications |
| title_short | Process Knowledge Graphs (PKG): Towards unpacking and repacking AI applications |
| title_sort | process knowledge graphs pkg towards unpacking and repacking ai applications |
| topic | Knowledge graphs Prompt engineering Data science pipelines Data pipelines documentation Data pipelines design |
| url | http://www.sciencedirect.com/science/article/pii/S1570826824000325 |
| work_keys_str_mv | AT enricodaga processknowledgegraphspkgtowardsunpackingandrepackingaiapplications |