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Comparing Statistical and Data Mining Techniques for Enrichment Ontology with Instances

Author

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  • Aurawan Imsombut
  • Jesada Kajornrit

    (College of Creative Design and Entertainment Technology, Dhurakij Pundit University, Bangkok, Thailand)

Abstract

Enriching instances into an ontology is an important task because the process extends knowledge in ontology to cover more extensively the domain of interest, so that greater benefits can be obtained. There are many techniques to classify instances of concepts with two popular techniques being the statistical and data mining methods. The paper compares the use of the two methods to classify instances to enrich ontology having greater domain knowledge, and selects a conditional random field for the statistical method and feature-weight k-nearest neighbor classification for the data mining method. The experiments are conducted on tourism ontology. The results show that conditional random fields methods provide greater precision and recall value than the other, specifically, F1-measure is 74.09% for conditional random fields and 60.04% for feature-weight k-nearest neighbor classification.

Suggested Citation

  • Aurawan Imsombut & Jesada Kajornrit, 2017. "Comparing Statistical and Data Mining Techniques for Enrichment Ontology with Instances," Journal of Reviews on Global Economics, Lifescience Global, vol. 6, pages 375-379.
    • Handle: RePEc:lif:jrgelg:v:6:y:2017:p:375-379
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    File URL: http://www.lifescienceglobal.com/independent-journals/journal-of-reviews-on-global-economics/volume-6/85-abstract/jrge/2825-abstract-comparing-statistical-and-data-mining-techniques-for-enrichment-ontology-with-instances
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    More about this item

    Keywords

    Ontology Enrichment; Statistical Technique; Classification; Conditional Random Fields (CRFs); Feature-weighted k-Nearest Neighbor.;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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