  With the growing popularity of large-scale collaborative ontology-engineering
projects, such as the creation of the 11th revision of the International
Classification of Diseases, we need new methods and insights to help project-
and community-managers to cope with the constantly growing complexity of such
projects. In this paper, we present a novel application of Markov chains to
model sequential usage patterns that can be found in the change-logs of
collaborative ontology-engineering projects. We provide a detailed presentation
of the analysis process, describing all the required steps that are necessary
to apply and determine the best fitting Markov chain model. Amongst others, the
model and results allow us to identify structural properties and regularities
as well as predict future actions based on usage sequences. We are specifically
interested in determining the appropriate Markov chain orders which postulate
on how many previous actions future ones depend on. To demonstrate the
practical usefulness of the extracted Markov chains we conduct sequential
pattern analyses on a large-scale collaborative ontology-engineering dataset,
the International Classification of Diseases in its 11th revision. To further
expand on the usefulness of the presented analysis, we show that the collected
sequential patterns provide potentially actionable information for
user-interface designers, ontology-engineering tool developers and
project-managers to monitor, coordinate and dynamically adapt to the natural
development processes that occur when collaboratively engineering an ontology.
We hope that presented work will spur a new line of ontology-development tools,
evaluation-techniques and new insights, further taking the interactive nature
of the collaborative ontology-engineering process into consideration.
