Abstract
Supporting knowledge-intensive processes (KiPs) has been widely addressed so far and is still subject of discussion. In this context, little attention was paid to the ontology-driven combination of data-centric and semantic business process modeling, which finds its motivation in supporting KiPs by enabling work sharing between humans and artificial intelligence. Such approaches have characteristics that could allow support for KiPs based on inferencing capabilities of reasoners. We confirm this as we show that reasoners are able to infer the executability of tasks. This is done by designing an inference mechanism to extend a currently researched ontology- and data-driven business process model (ODD-BP model). Further support for KiPs by the proposed inference mechanism results from its ability to infer the relevance of tasks, depending on the extent to which their execution would contribute to process progress. Thereby, it takes into account the dynamic behaviour of KiPs and helps knowledge workers to pursue their process goals.
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Notes
- 1.
SEMANAS is funded by the Federal Ministry of Education and Research (BMBF), grant no. 13FH013IX6, duration: 2017–2021.
- 2.
OWL 2 specification: https://www.w3.org/TR/owl2-syntax/.
- 3.
Protégé is a free-to-use Stanford University ontology editor, available online at: https://protege.stanford.edu/.
- 4.
Pellet is released under Dual Licensing and available online at: https://github.com/stardog-union/pellet.
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Maletzki, C., Rietzke, E., Grumbach, L., Bergmann, R., Kuhn, N. (2019). Utilizing Ontology-Based Reasoning to Support the Execution of Knowledge-Intensive Processes. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds) Business Process Management Workshops. BPM 2019. Lecture Notes in Business Information Processing, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-030-37453-2_4
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