IJMLC 2019 Vol.9(5): 700-705 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.5.861

Malaysian Food Composition Ontology Evaluation

Norlia M. Yusof and Shahrul Azman M. Noah

Abstract—The Malaysian food composition ontology (MyFCO) models a dietitian’s knowledge in designing dietary menu planning. Ontology Development 101 is the method of ontology modeling. The objective of this paper was to share the experience in evaluating MyFCO. It specifically focused on the validation activity using OOPS! tools. The results obtained from OOPS! showed that MyFCO was free from critical error; however, it had three important and six minor pitfalls. Four pitfalls were repaired, whereas the others were remained. The integration between automatic and conventional validation` approaches enhanced the quality of ontology being modeled. The tools improved the conventional approach with faster, easier, and less subjective of a diagnosis activity. Whereas for repair activity, it recommended solutions for the pitfalls.

Index Terms—Food ontology, ontology evaluation, ontology validation, ontology diagnosis, ontology repair, OOPS!.

The authors are with the Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia (e-mail: norlia@ siswa.ukm.edu.my, shahrul@ukm.edu.my).


Cite: Norlia M. Yusof and Shahrul Azman M. Noah, "Malaysian Food Composition Ontology Evaluation," International Journal of Machine Learning and Computing vol. 9, no. 5, pp. 700-705, 2019.

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net