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Autonomy in the Age of Knowledge Graphs: Vision and Challenges

Authors Jean-Paul Calbimonte , Andrei Ciortea , Timotheus Kampik , Simon Mayer , Terry R. Payne , Valentina Tamma , Antoine Zimmermann

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Author Details

Jean-Paul Calbimonte
  • University of Applied Sciences and Arts Western Switzerland HES-SO, Sierre, Switzerland
Andrei Ciortea
  • Institute of Computer Science, University of St.Gallen, Switzerland
Timotheus Kampik
  • Department of Computing Science, Umeå University, Sweden
  • SAP Signavio, Berlin, Germany
Simon Mayer
  • Institute of Computer Science, University of St.Gallen, Switzerland
Terry R. Payne
  • Department of Computer Science, University of Liverpool, United Kingdom
Valentina Tamma
  • Department of Computer Science, University of Liverpool, United Kingdom
Antoine Zimmermann
  • Mines Saint-Etienne, Université Clermont Auvergne, INP Clermont Auvergne, CNRS, UMR 6158 LIMOS, France

Funding

This research has received funding from the Swiss National Science Foundation under grant No. 189474 (HyperAgents), grant No. 213369 (StreamDKG), from the European Union’s Horizon 2020 research and innovation program under grant No. 957218 (IntellIoT), and from the Horizon Europe program under grant No. 101092908 (SmartEdge).

Acknowledgements

We would like to thank Chiara Ghidini for the useful discussions on modelling processes using KGs to support process execution, mining and discovery.

Cite AsGet BibTex

Jean-Paul Calbimonte, Andrei Ciortea, Timotheus Kampik, Simon Mayer, Terry R. Payne, Valentina Tamma, and Antoine Zimmermann. Autonomy in the Age of Knowledge Graphs: Vision and Challenges. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 13:1-13:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/TGDK.1.1.13

Abstract

In this position paper, we propose that Knowledge Graphs (KGs) are one of the prime approaches to support the programming of autonomous software systems at the knowledge level. From this viewpoint, we survey how KGs can support different dimensions of autonomy in such systems: For example, the autonomy of systems with respect to their environment, or with respect to organisations; and we discuss related practical and research challenges. We emphasise that KGs need to be able to support systems of autonomous software agents that are themselves highly heterogeneous, which limits how these systems may use KGs. Furthermore, these heterogeneous software agents may populate highly dynamic environments, which implies that they require adaptive KGs. The scale of the envisioned systems - possibly stretching to the size of the Internet - highlights the maintainability of the underlying KGs that need to contain large-scale knowledge, which requires that KGs are maintained jointly by humans and machines. Furthermore, autonomous agents require procedural knowledge, and KGs should hence be explored more towards the provisioning of such knowledge to augment autonomous behaviour. Finally, we highlight the importance of modelling choices, including with respect to the selected abstraction level when modelling and with respect to the provisioning of more expressive constraint languages.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Multi-agent systems
  • Computing methodologies → Intelligent agents
  • Computer systems organization → Self-organizing autonomic computing
  • Computing methodologies → Knowledge representation and reasoning
  • Information systems → Semantic web description languages
Keywords
  • Knowledge graphs
  • Autonomous Systems

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