Analysis of User Editing Patterns in Ontology Development Projects

The development of real-world ontologies is a complex undertaking, commonly involving a group of domain experts with different expertise that work together in a collaborative setting. These ontologies are usually large scale and have a complex structure. To assist in the authoring process, ontology tools are key at making the editing process as streamlined as possible. Being able to predict confidently what the users are likely to do next as they edit an ontology will enable us to focus and structure the user interface accordingly and to enable more efficient interaction and information discovery. In this paper, we use data mining techniques to investigate whether we are able to predict the next editing operation that a user will make based on the change history. We have analyzed the change logs of two real-world biomedical ontologies, and used association rule mining to find editing patterns using different features. We evaluated the prediction accuracy on a test set of change logs for these two ontologies. Our results indicate that we can indeed predict the next editing operation a user is likely to make. We will use the discovered editing patterns to develop a recommendation module for our editing tools, and to design user interface components that are better fitted with the user editing behaviors.