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Walk, Simon; Pöschko, Jan; Strohmaier, Markus; Andrews, Keith; Tudorache, Tania; Noy, Natalya F.; Nyulas, Csongor; Musen, Mark A.

doi:10.4018/jswis.2013010103

Cited by 11 publications

(1 citation statement)

References 27 publications

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“…We and other investigators have used change data from ontologies to measure the level of community activities in biomedical ontologies [14], to migrate data from an old version of an ontology to a new one [12], and to analyze user roles in the process of collaboration [6,7,23,26]. For example, we have demonstrated that we can use the change data to assess the level of stabilization in ontology content [26], to find implicit user roles [7], and to describe the collaboration qualitatively [23]. For example, we found that changes to ICD-11 tend to propagate along the class hierarchy: A user who alters a property value for a class is significantly more likely to make a change to a property value for a subclass of that class than to make an edit anywhere else in the ontology [19].…”

Section: Collaborative Ontology Development and Related Workmentioning

confidence: 99%

Analysis and Prediction of User Editing Patterns in Ontology Development Projects

et al. 2014

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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 complex structures. 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 facilitate more efficient interaction and information discovery. In this paper, we use data mining, specifically the association rule mining, to investigate whether we are able to predict the next editing operation that a user will make based on the change history. We simulated and evaluated continuous prediction across time using sliding window model. We used the association rule mining to generate patterns from the ontology change logs in the training window and tested these patterns on logs in the adjacent testing window. We also evaluated the impact of different training and testing window sizes on the prediction accuracies. At last, we evaluated our prediction accuracies across different user groups and different 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 better fit with the user editing behaviors.

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