Semantically enhanced case adaptation for dietary menu recommendation of diabetic patients

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.