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Procedural knowledge describes how to accomplish tasks and mitigate problems.Such knowledge is commonly held by domain experts, e.g. operators inmanufacturing who adjust parameters to achieve quality targets. To the best ofour knowledge, no real-world datasets containing process data and correspondingprocedural knowledge are publicly available, possibly due to corporateapprehensions regarding the loss of knowledge advances. Therefore, we provide aframework to generate synthetic datasets that can be adapted to differentdomains. The design choices are inspired by two real-world datasets ofprocedural knowledge we have access to. Apart from containing representationsof procedural knowledge in Resource Description Framework (RDF)-compliantknowledge graphs, the framework simulates parametrisation processes andprovides consistent process data. We compare established embedding methods onthe resulting knowledge graphs, detailing which out-of-the-box methods have thepotential to represent procedural knowledge. This provides a baseline which canbe used to increase the comparability of future work. Furthermore, we validatethe overall characteristics of a synthesised dataset by comparing the resultsto those achievable on a real-world dataset. The framework and evaluation code,as well as the dataset used in the evaluation, are available open source.

PDPK: A Framework to Synthesise Process Data and Corresponding Procedural Knowledge for Manufacturing