An Ontology and SWRL-Based Framework for Healthy Food Recommendation in Middle Childhood

Z. K. A. Baizal; A. F. Ihsan; P. D. Kusuma; R. Dharayani; P. H. Gani

doi:10.3991/ijoe.v22i07.60745

Authors

Z. K. A. Baizal Telkom University, Bandung, Indonesia https://orcid.org/0000-0003-0795-9559
A. F. Ihsan Telkom University, Bandung, Indonesia https://orcid.org/0000-0002-1201-7198
P. D. Kusuma Telkom University, Bandung, Indonesia
R. Dharayani Telkom University, Bandung, Indonesia
P. H. Gani Telkom University, Bandung, Indonesia https://orcid.org/0009-0000-7263-0253

DOI:

https://doi.org/10.3991/ijoe.v22i07.60745

Keywords:

recommender system, conversational recommender system, middle childhood, Ontology, semantic web rule language

Abstract

Food recommender systems have garnered attention in various research domains. Despite extensive research, the development of healthy food recommender systems specifically targeting middle childhood age children remains scarce. This age group is susceptible to critical transitions, and mismanagement can lead to rapid weight gain, type 2 diabetes, and cardiovascular diseases. To address this gap, we propose a framework for developing a healthy food conversational recommender system tailored to middle childhood age. Our framework comprises ontology and semantic web rule language (SWRL) rules, facilitating knowledge representation and reasoning within the system. We built it by referring to the Nutritional Adequacy Rate from the Indonesian Ministry of Health and typical Indonesian cuisine. The system suggests nutritious options for breakfast, lunch, dinner, and snacks. The adaptability of this framework extends beyond Indonesia, making it suitable for constructing recommender systems tailored to various countries. By adjusting the ontology’s data to accommodate each country’s culinary landscape, the system becomes versatile enough to cater to diverse cultural dietary preferences, offering personalized recommendations irrespective of geographical boundaries. The evaluation is focused on validating the proposed framework. The results of accuracy testing show that the knowledge design has an accuracy rate of 83.33%.

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