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Sequential Action Patterns in Collaborative Ontology-Engineering Projects: A Case-Study in the Biomedical Domain

Within the last few years the importance of collaborative ontology-engineering projects, especially in the biomedical domain, has drastically increased. This recent trend is a direct consequence of the growing complexity of these structured data representations, which no single individual is able to handle anymore. For example, the World Health Organization is currently actively developing the next revision of the International Classification of Diseases (ICD), using an OWL-based core for data representation and Web 2.0 technologies to augment collaboration. This new revision of ICD consists of roughly 50,000 diseases and causes of death and is used in many countries around the world to encode patient history, to compile health-related statistics and spendings. Hence, it is crucial for practitioners to better understand and steer the underlying processes of how users collaboratively edit an ontology. Particularly, generating predictive models is a pressing issue as these models may be leveraged for generating recommendations in collaborative ontology-engineering projects and to determine the implications of potential actions on the ontology and community. In this paper we approach this task by (i) exploring whether regularities and common patterns in user action sequences, derived from change-logs of five different collaborative ontology-engineering projects from the biomedical domain, exist. Based on this information we (ii) model the data using Markov chains of varying order, which are then used to (iii) predict user actions in the sequences at hand.