Diabetes is a non-communicable disease that has reached epidemic proportions, affecting 537 million people globally. Artificial Intelligence can support patients or clinicians in diabetes nutrition therapy - the first medical therapy in most cases of Type 1 and Type 2 diabetes. In particular, ontology-based recommender and decision support systems can deliver a computable representation of experts' knowledge, thus delivering patient-tailored nutritional recommendations or supporting clinical personnel in identifying the most suitable diet. This work proposes a systematic literature review of the domain ontologies describing diabetes in such systems, identifying their underlying conceptualizations, the users targeted by the systems, the type(s) of diabetes tackled, and the nutritional recommendations provided. This review also delves into the structure of the domain ontologies, highlighting several aspects that may hinder (or foster) their adoption in recommender and decision support systems for diabetes nutrition therapy. The results of this review process allow to underline how recommendations are formulated and the role of clinical experts in developing domain ontologies, outlining the research trends characterizing this research area. The results also allow for identifying research directions that can foster a preeminent role for clinical experts and clinical guidelines in a cooperative effort to make ontologies more interoperable - thus enabling them to play a significant role in the decision-making processes about diabetes nutrition therapy.

Research in the fields of the Smart Home and Ambient Assisted Living has increased in the last decade. While some solutions are available to general public, there are still some concerns related to the design of smart solutions and their acceptance, especially when it comes to residents' monitoring and privacy. In this context, this paper introduces an ontology-based smart home framework, LIDoMS, aimed at focusing on inhabitants needs by providing a representation of their health conditions, while offering them customized services. End-users can interact with the system thanks to an adaptive and ubiquitous graphical interface. In this work, the ontological framework underlying LIDoMS is described; the system exploits the knowledge regarding the status of appliances and residents' location within the smart home to infer the activity they are involved in, and to provide customized adaptation of indoor comfort metrics, thus enabling a less invasive monitoring of the inhabitants. Two scenarios describe how different residents can interact with LIDoMS to personalize comfort metrics - pivotal for their health condition. Finally, a framework for the validation of LIDoMS is presented.