# Ontology-Based Nutritional Recommender System
## 5 Conclusions and Future Work  
In the medical community, obesity is considered as a chronic illness that comes from a multifactorial etiology [59]. This disorder is characterized by the unchecked increase of fat tissue in the body. As a preventable disorder, we need to develop mechanisms that help people overcome it. Therefore, we believe that the knowledge representation techniques are remarkably useful to that end. It provides suitable elements such as concepts, object, and data properties to accurately define the data in this domain. Its usage provides the opportunity to the biomedical community to mitigate this disorder with consistent, reusable, and standardized descriptions about obesity and dietetic food. In this work, two main contributions can be identified. On the one hand, the recommender system is capable of making suggestions considering certain users' diet restrictions depending on their medical condition. On the other hand, we implemented the Ontology of Dietary Recommendations (ODR), which concretely integrates information about obesity, nutrition, food, and health. In addition, a couple of algorithms were implemented to convert heterogeneous data into semantic axioms, such as user profiles and recipe ingredients. We expect this conceptual model will help the intelligent systems and the medical profession to exploit this knowledge base in different ways. This work aimed at providing general diet suggestions without considering users' requirements such as tastes, food restrictions, diseases, and allergies, which represent a significant constraint. Furthermore, our approach does not provide a dietetic diary for users, which means that there is no dynamic readjustment, but instead, this adaptation must be carried out manually. We plan to address these limitations as part of future work. Likewise, the way recipes are modeled in the ontology will be enriched by including more details concerning the nutritional properties, thus facilitating the development of different added-value features. So far, each recipe is characterized by properties such as _has_fat_, _has_calories_, _has_sodium_, _has_cholesterol_, or _has_protein_. Some other attributes such as energy, fiber content, and carbohydrate content can be defined in the \"Recipe\" ontology concept so as to enable the system to manage new relevant indicators and provide better recommendations. That additional information can be gathered from publicly available databases such as Open Food Facts ([[https://world.openfoodfacts.org/](https://world.openfoodfacts.org/)]([https://world.openfoodfacts.org/](https://world.openfoodfacts.org/)) (accessed on 12 December 2021)). For the proof-of-concept implementation of the system presented in this manuscript, the knowledge base was populated relying on some scientific publications and information gathered from the Internet. For it to be deployed to customers, nutrition experts should be consulted to review and update the knowledge base by including only those diets and recipes that are endorsed by the scientific community and defining the most accurate and feasible indicators to determine the (nutritional) health condition of individuals. Indeed, as pointed out above, research has shown that the BMI alone is not the best tool to assess the grade of obesity [60]. A better understanding of the individuals' overall health status and nutritional requirements can be obtained by combining different markers. Finally, the _Food Allergies_ concept was included in the ontology, but this contribution does not go far enough. We believe that it would be interesting for end users to axiomatically expand the ontological hierarchy defining more specific user profile diets by considering allergens and, consequently, to provide a better recommendation service.