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Semantically Enhanced Case Adaptation for Dietary Menu Recommendation of Diabetic Patients

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Semantic Technology (JIST 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10675))

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Abstract

Dietary menu planning for diabetic patients is a complicated tasks involving specific and common-sense knowledge. Case-based approach has been used to provide recommendation in the case where ratings were not easily available for domains such as menu planning. Among the important but yet difficult tasks in the case-based approach is case adaptation. To successfully support case adaptation, the constraint-based approach and food composition ontology were employed. Constraints knowledge were represented as production rules and exploits the food ontology to support adaptation. An ontological approach is also proposed to perform the inference process to satisfy the multiple design constraints.

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Notes

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    https://protege.stanford.edu/.

  2. 2.

    http://gaia.fdi.ucm.es/research/colibri/jcolibri.

  3. 3.

    https://jena.apache.org/.

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Acknowledgement

This research is partially supported by the Malaysia Ministry of Education Grant FRGS/1/2014/ICT02/UKM/01/1 awarded to the Center for Artificial Intelligence Technology at the Universiti Kebangsaan Malaysia.

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Authors and Affiliations

  1. Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia

    Norlia Mohd Yusof & Shahrul Azman Mohd Noah

Authors
  1. Norlia Mohd Yusof
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  2. Shahrul Azman Mohd Noah
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Corresponding author

Correspondence to Shahrul Azman Mohd Noah .

Editor information

Editors and Affiliations

  1. Griffith University, Brisbane, Queensland, Australia

    Zhe Wang

  2. Dresden University of Technology, Dresden, Germany

    Anni-Yasmin Turhan

  3. Griffith University, Brisbane, Queensland, Australia

    Kewen Wang

  4. Tianjin University, Tianjin, China

    Xiaowang Zhang

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Yusof, N.M., Noah, S.A.M. (2017). Semantically Enhanced Case Adaptation for Dietary Menu Recommendation of Diabetic Patients. In: Wang, Z., Turhan, AY., Wang, K., Zhang, X. (eds) Semantic Technology. JIST 2017. Lecture Notes in Computer Science(), vol 10675. Springer, Cham. https://doi.org/10.1007/978-3-319-70682-5_22

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  • DOI: https://doi.org/10.1007/978-3-319-70682-5_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70681-8

  • Online ISBN: 978-3-319-70682-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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