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

Hao Wang1, Tania Tudorache2, Dejing Dou1

  • 1Department of Computer and Information Science, 1202 University of Oregon, Eugene, OR 97403, USA.

Journal on Data Semantics
|June 9, 2015
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Summary
This summary is machine-generated.

This study demonstrates that association rule mining can predict users' next ontology editing actions based on change history. This enables the development of smarter ontology editing tools and improved user interfaces for efficient interaction.

Keywords:
Collaborative Ontology DevelopmentData Mining Association Rule MiningOntologyUser Editing Pattern

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Area of Science:

  • Knowledge Representation and Reasoning
  • Human-Computer Interaction
  • Data Mining

Background:

  • Ontology development is a complex, collaborative process requiring specialized tools.
  • Streamlining ontology authoring enhances efficiency and information discovery.
  • Predicting user actions can optimize ontology editing interfaces.

Purpose of the Study:

  • To investigate the predictability of the next user editing operation in ontology development using data mining.
  • To evaluate the effectiveness of association rule mining for predicting user actions based on historical data.
  • To assess the impact of various parameters on prediction accuracy.

Main Methods:

  • Utilized association rule mining on ontology change logs.
  • Employed a sliding window model for continuous time-based prediction.
  • Generated patterns from training data and tested on adjacent testing data.
  • Evaluated prediction accuracy across different window sizes, user groups, and ontologies.

Main Results:

  • Demonstrated the capability to predict the next user editing operation with significant accuracy.
  • Confirmed the effectiveness of association rule mining in identifying user editing patterns.
  • Showcased that prediction accuracy is influenced by training/testing window sizes, user groups, and specific ontologies.

Conclusions:

  • User editing behaviors in ontology development are predictable.
  • Discovered editing patterns can inform the creation of recommendation modules for ontology tools.
  • Findings support the design of user interfaces tailored to user editing behaviors for enhanced usability.