Abstract
Knowledge Graphs (KGs) are a powerful tool for representing domain knowledge in a way that is interpretable for both humans and machines. They have emerged as enablers of semantic integration in various domains, including Business Process Modeling (BPM). However, existing KG-based approaches in BPM lack the ability to capture dynamic process executions. Rather, static components of BPM models, such as Business Process Model and Notation (BPMN) elements, are represented as KG instances and further enriched with static domain knowledge. This poses a challenge as most business processes exhibit inherent degrees of freedom, leading to variations in their executions. To address this limitation, we examine the semantic modeling of BPMN terminology, models, and executions within a shared KG to facilitate the inference of new insights through observations of process executions. We address the issue of representing BPMN models within the concept or instance layer of a KG, comparing potential implementations and outlining their advantages and disadvantages in the context of a human-AI collaboration use case from a European smart manufacturing project.
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This work is part of the TEAMING.AI project which receives funding in the European Commission’s Horizon 2020 Research Programme under Grant Agreement Number 957402 (www.teamingai-project.eu).
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Krause, F., Kurniawan, K., Kiesling, E., Paulheim, H., Polleres, A. (2023). On the Representation of Dynamic BPMN Process Executions in Knowledge Graphs. In: Ortiz-Rodriguez, F., Villazón-Terrazas, B., Tiwari, S., Bobed, C. (eds) Knowledge Graphs and Semantic Web. KGSWC 2023. Lecture Notes in Computer Science, vol 14382. Springer, Cham. https://doi.org/10.1007/978-3-031-47745-4_8
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