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_40: The World Health Organization is beginning to use Semantic Web technologies in the development of the 11th revision of the International Classification of Diseases (ICD-11). Health officials use ICD in all United Nations member countries to compile basic health statistics, to monitor health-related spending, and to inform policy makers. While previous revisions of ICD encoded minimal information about a disease, and were mainly published as books and tabulation lists, the creators of ICD-11 envision that it will become a multipurpose and coherent classification ready for electronic health records. Most important, they plan to have ICD-11 applied for a much broader variety of uses than previous revisions. The new requirements entail significant changes in the way we represent disease information, as well as in the technologies and processes that we use to acquire the new content. In this paper, we describe the previous processes and technologies used for developing ICD. We then describe the requirements for the new development process and present the SemanticWeb technologies that we use for ICD-11. We outline the experiences of the domain experts using the software system that we implemented using Semantic Web technologies. We then discuss the benefits and challenges in following this approach and conclude with lessons learned from this experience.
@cite_6: In this paper we present GOSPL, which stands for Grounding Ontologies with Social Processes and Natural Language. GOSPL is a method and tool that supports stakeholders in iteratively interpreting and modeling their common hybrid ontologies using their own terminology for semantic interoperability between autonomously developed and maintained information systems. Hybrid ontologies are ontologies in which concepts are both formally and informally described with the help of a special linguistic resource called glossary. Social interactions between the community members drive the ontology evolution process and result in more stable and agreed upon ontologies.
, and its extensions for collaborative development, such as and iCAT @cite_6 (see Figure for a screenshot of the iCAT ontology-editor interface) are prominent tools that are used by a large community worldwide to develop ontologies in a variety of different projects. Both and provide a robust and scalable environment for collaboration and are used in several large-scale projects, including the development of ICD-11 @cite_40 .