Abstract: 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.

The use of ontologies to model human behaviours that affect health is challenging since this process involves data from multiple inter-related domains that unfold and evolve over time. However, while current ontology development methodologies are generic enough to model any domain of interest, they do not provide design guidelines for modelling time-related aspects. This paper proposes a methodology for ontology development that entails the requirements for behaviours modelling based on passive temporal data. Its main focus is on temporal representations of classes and their holistic relations since no other methodology approaches ontology design from its temporal perspective. We exemplify these ideas by modelling the sleep behaviour domain, its relations to other behavioural aspects, and its effects on health.

Abstract: Magnetic resonance imaging (MRI) is commonly used to diagnose and follow the progress of brain tumours. However, they are expensive, not easily accessible, and require frequent visits of patients to specialised health centres. This paper investigates the use of passively assessed gait data as a proxy for MRI. Three hypotheses were evaluated: (1) There exists a set of gait features more sensitive to identify brain tumours; (2) The evolution of gait is correlated with the progress of brain tumours; and (3) Locations of brain tumours are also correlated to gait features. Data from UK Biobank healthy individuals (N=240 x 7 days) were used to define a baseline for gait analysis. Then, we evaluated the sensitivity of 28 gait features, which were used to differentiate this healthy population from brain tumours patients (BrainWear project, N=49 x 9 months). We evaluated several learning strategies to create static models that could identify the existence and position of brain tumours, while the longitudinal use of these models was evaluated to correlate gait patterns and tumour progress. Statistical tests demonstrated that healthy individuals and cancer patients present significant differences (p<0.05) regarding subsets of gait features. Moreover, classification models presented a maximum accuracy of 77% to differentiate these two groups. However, the tumours’ location identification was inaccurate (33% - 65%), mainly for some regions (e.g., right temporal). We also found correlation evidence between gait patterns and tumours progress and treatments, showing that the use of passive gait analysis has the potential to corroborate with MRI diagnoses.