A Systematic Review of Ontologies in Human Nutrition: Methods, Applications, and Challenges With Preliminary Knowledge Graph Analysis

Abstract

Over the past decade, knowledge resources have become central to advancing digital health, particularly in nutrition research. Nutrition is crucial for human growth, development, metabolism, and immunity, making it a key focus in public health and personalized medicine. Ontologies and Knowledge Graphs (KGs) have emerged as essential tools for addressing challenges in this field. This systematic review, conducted following the PRISMA guidelines, focuses primarily on the use of ontologies in human nutrition, including their applications, development methodologies, construction resources, evaluation criteria, and associated challenges. KGs are considered in a secondary role to support the preliminary analysis and provide insights into related applications. The review covers studies published between 2014 and 2024. A comprehensive search across Scopus, Web of Science, IEEE Xplore, and ACM Digital Library identified 58 studies, including 45 utilizing ontologies and 13 employing KGs. The findings reveal a growing use of these tools to develop personalized recipes, nutrient intake recommendations, food suggestions, dietary plans, and recommendation systems. However, significant gaps persist in the standardization of methodologies, resource integration, and evaluation criteria, limiting interoperability and scalability for personalized applications. Ontology-based systems dominate knowledge representation, while KGs are being explored for personalization in diet planning and data integration. To our knowledge, this is the first systematic review to exclusively focus on the intersection of ontologies with a preliminary analysis of KGs in human nutrition. By bridging computational techniques with nutritional science, this review establishes the foundation for innovative applications in digital health and personalized nutrition, contributing to improved public health outcomes.