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Data-driven Ontology Engineering Framework

Steve Leung, Oscar Lin, Dunwei Wen ICCE, 2008, Taipei. Data-driven Ontology Engineering Framework. The Problem. Need to find a machine readable job objectives for courses planning. Courses planning depends on a complex and dynamic factors.

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Data-driven Ontology Engineering Framework

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  1. Steve Leung, Oscar Lin, Dunwei Wen ICCE, 2008, Taipei Data-driven Ontology Engineering Framework

  2. The Problem • Need to find a machine readable job objectives for courses planning. • Courses planning depends on a complex and dynamic factors. • Predetermined categories of job objectives do not work: • Perceived meanings • More than one category

  3. The idea

  4. The design

  5. Advantages • Data-driven • Limited human intervention • Human intervention does required! • Automation • No need to integrate new and existing ontologies • Self correction and adaptation

  6. Assumptions and Limitations • Unknown universe of terms • Light-weight ontology • Application oriented • Rigorous sampling method required • Human expertise • Clustering method and distance functions • Majority threshold • Consistency maintenance and versioning

  7. Interim results • E-advisor • Survey • Population: all MSc IS students • Questionnaires: two open-ended questions • 135 valid respondents • 10 clusters • Two concepts

  8. Future works • Quality guidelines • Users evaluation • Versioning system

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