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. HighlightsWe provide a novel application for Markov.Using Markov chains we extract and analyze sequential usage patterns.We categorize the types of analyses that Markov chains enable us to perform.We demonstrate the utility of the Markov chain analysis on a large-scale project.
@cite_24: We examine the procedural side of Wikipedia, the well-known internet encyclopedia. Despite the lack of structure in the underlying wiki technology, users abide by hundreds of rules and follow well-defined processes. Our case study is the Featured Article (FA) process, one of the best established procedures on the site. We analyze the FA process through the theoretical framework of commons governance, and demonstrate how this process blends elements of traditional workflow with peer production. We conclude that rather than encouraging anarchy, many aspects of wiki technology lend themselves to the collective creation of formalized process and policy.
Further, have shown that the history of an article and discussion pages in Wikipedia contain valuable information for administrators and moderators. In @cite_24 the authors conclude that collectives in Wikipedia follow their self-imposed rules regarding well defined and formalized processes, such as featured articles. discussed multiple different aspects and the importance of consensus finding on Wikipedia and the Social Semantic Web, by analyzing the history of articles in said systems, further strengthening the need for tools and analyses to be able to better understand and support digital collaborative endeavors.