Healthy Food Recommender System for Obesity Using Ontology and Semantic Web Rule Language

Abstract

Today's lifestyle and eating patterns tend to be irregular due to busyness. People prefer eating foods that are fast and easy to obtain, but often lack knowledge of the nutritional content in them. These eating patterns lead to unbalanced nutrition and can cause various health problems and diseases, such as overweight and obesity. Due to a lack of information, people often turn to drugs instead of learning about healthy diets, making it difficult for them to determine what menu to choose or what type of food to consume. While there have been many studies to recommend healthy food based on user preferences, there is currently no recommender system that includes serving size and budget for each daily food recommendation that is implemented in a chatbot framework. This study proposes using ontology and the Semantic Web Rule Language (SWRL) to store knowledge in the ontology and then process it using SWRL to produce food recommendations based on user preferences. From a sample of user data which obtained 170 recommended meal menus. System performance is pretty good. Based on the validation results from nutritionists, the precision value was 0.852941, the recall was 1, and F-score of 0.920634 So that a healthy food recommendation system can be used to help the user follows a diet that meets his nutritional needs and is within his budget needed

Ontology-Based Physical Exercise Recommender System for Underweight Using Ontology and Semantic Web Rule Language

“…Semantic Rule Web Language is generally an OWL-based language for presenting generated rules. SWRL combines OWL knowledge base and inference rules to perform reasoning on the basis of OWL ontology [9]. Ontology-based approaches, such as [10]- [12], have been implemented to generate recommendations using domain knowledge and rules.…”

Ontology-Based Food Menu Recommender System for Patients with Coronary Heart Disease

“…In addition, the performance evaluation of the dietary recommendation system using the ontology of each individual was compared with the recommendations provided by health advisors, achieving an average accuracy of 87% (Mckensy-Sambola et al, 2021). (Aditya et al, 2023) used ontology with SWRL in developing a recommender system creating food recommendations based on what users like in helping users implement appropriate healthy eating patterns.…”