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Wang H, Tudorache T, Dou D, et al. Analysis and Prediction of User Editing Patterns in Ontology Development Projects. J Data Semant. 2015;4(2):117-132doi: 10.1007/s13740-014-0047-3.
Wang, H., Tudorache, T., Dou, D., Noy, N. F., & Musen, M. A. (2015). Analysis and Prediction of User Editing Patterns in Ontology Development Projects. Journal on data semantics, 4(2), 117-132. https://doi.org/10.1007/s13740-014-0047-3
Wang, Hao, et al. "Analysis and Prediction of User Editing Patterns in Ontology Development Projects." Journal on data semantics vol. 4,2 (2015): 117-132. doi: https://doi.org/10.1007/s13740-014-0047-3
Wang H, Tudorache T, Dou D, Noy NF, Musen MA. Analysis and Prediction of User Editing Patterns in Ontology Development Projects. J Data Semant. 2015 Jun;4(2):117-132. doi: 10.1007/s13740-014-0047-3. PMID: 26052350; PMCID: PMC4454427.
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J Data Semant. 2015 Jun;4(2):117-132. doi: 10.1007/s13740-014-0047-3.

Analysis and Prediction of User Editing Patterns in Ontology Development Projects.

Journal on data semantics

Hao Wang, Tania Tudorache, Dejing Dou, Natalya F Noy, Mark A Musen

Affiliations

  1. Department of Computer and Information Science, 1202 University of Oregon, Eugene, OR 97403, USA.
  2. Stanford Center for Biomedical Informatics Research, Stanford University, 1265 Welch Road, Stanford, CA 94305, USA.

PMID: 26052350 PMCID: PMC4454427 DOI: 10.1007/s13740-014-0047-3

Abstract

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.

Keywords: Collaborative Ontology Development; Data Mining Association Rule Mining; Ontology; User Editing Pattern

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Grant support