  Procedural knowledge describes how to accomplish tasks and mitigate problems.
Such knowledge is commonly held by domain experts, e.g. operators in
manufacturing who adjust parameters to achieve quality targets. To the best of
our knowledge, no real-world datasets containing process data and corresponding
procedural knowledge are publicly available, possibly due to corporate
apprehensions regarding the loss of knowledge advances. Therefore, we provide a
framework to generate synthetic datasets that can be adapted to different
domains. The design choices are inspired by two real-world datasets of
procedural knowledge we have access to. Apart from containing representations
of procedural knowledge in Resource Description Framework (RDF)-compliant
knowledge graphs, the framework simulates parametrisation processes and
provides consistent process data. We compare established embedding methods on
the resulting knowledge graphs, detailing which out-of-the-box methods have the
potential to represent procedural knowledge. This provides a baseline which can
be used to increase the comparability of future work. Furthermore, we validate
the overall characteristics of a synthesised dataset by comparing the results
to 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.
