Engineering uncertain time for its practical integration in ontologies

Ontologies are commonly used as a strategy for knowledge representation. However, they are still presenting limitations to model domains that require broad forms of temporal reasoning. This study is part of the Onto-mQoL project and was motivated by the real need to extend static ontologies with diverse time concepts, relations and properties, which go beyond the commonly used Allen´s Interval Algebra. Therefore, we use the n-ary relations as the basis for temporal structures, which minimally modify the original ontology, and extend these structures with a generic set of time concepts (moments and intervals), time concept properties (precise and uncertain), time relations (interval-interval, interval-moment, and moment-moment), and time relation properties (qualitative and quantitative). We divided the scientific contribution of this study into three parts. Firstly, we present the ontological temporal model (classes and properties) and how it is integrated into static ontologies. Secondly, we discuss the creation of axioms that give the semantics for precise temporal elements. Finally, as our main contribution, these ideas are extended with axioms for uncertain time. All these elements follow the Ontology Web Language (OWL) standards, so this proposal is still compatible with the main ontology editors and reasoners currently available. A case example demonstrates the use of this approach in the nutrition assessment domain.</p

Behavioral Data Categorization for Transformers-based Models in Digital Health

Transformers are recent deep learning (DL) models used to capture the dependence between parts of sequential data. While their potential was already demonstrated in the natural language processing (NLP) domain, emerging research shows transformers can also be an adequate modeling approach to relate longitudinal multi-featured continuous behavioral data to future health outcomes. As transformers-based predictions are based on a domain lexicon, the use of categories, commonly used in specialized areas to cluster values, is the likely way to compose lexica. However, the number of categories may influence the transformer prediction accuracy, mainly when the categorization process creates imbalanced datasets, or the search space is very restricted to generate optimal feasible solutions. This paper analyzes the relationship between models’ accuracy and the sparsity of behavioral data categories that compose the lexicon. This analysis relies on a case example that uses mQoL- Transformer to model the influence of physical activity behavior on sleep health. Results show that the number of categories shall be treated as a further transformer’s hyperparameter, which can balance the literature-based categorization and optimization aspects. Thus, DL processes could also obtain similar accuracies compared to traditional approaches, such as long short-term memory, when used to process short behavioral data sequence</p