Temporal data (TD) in Semantic Web are affected by different types of imperfections principally conflict. In the literature, most of the proposed approaches deal with perfect TD. However, to our knowledge, there is no approach to dealing with conflicting TD. In this paper, we propose an approach to represent and reason about quantitative conflicting TD (i.e., time intervals and points) and associated qualitative relations (e.g., “before”). This approach is based on evidence theory and it is three folds. (i) For the representation, the mass function of the conflicting temporal data is estimated, through the believability measures estimated based on our previous DBE_ALZ approach. Then, an ontology-based representation for the handled TD associated with the obtained mass function is proposed. (ii) For the reasoning, our approach relies on the Allen’s interval relations. First, we extend this algebra to reason about conflicting temporal relations. The resulting interval relations preserve the properties of the original algebra. Second, we adapt the proposed relations to define new ones relating a time interval and a time point, and two time points. All the proposed relations can be used for temporal reasoning through transitivity tables. (iii) Based on (i) and (ii), we propose a new evidential ontology named “BeliefTimeOnto”. We implement a prototype to ease the interaction with the proposed ontology. We conduct two case studies: the first is about temporal data entered by Alzheimer’s patients in the context of a memory prosthesis and the second is about data entered the context of Collective Memory application. The evaluation proves the usefulness of the proposed approach as all the inferences are well established and the precision results are interesting.

Les données temporelles fournies par les patients atteints Alzheimer sont sujettes à l’incertitude. De nombreuses approches ont été proposées pour traiter des données temporelles certaines, mais non pas celles qui sont incertaines. Cet article propose une approche pour représenter et raisonner sur des intervalles et des points de temps quantitatifs certains et incertains et les relations qualitatives entre eux. Elle inclut trois volets : (1) une extension de l’approche 4D-fluents avec de nouvelles composantes ontologiques pour représenter des données temporelles certaines et incertaines. (2) une extension de l’algèbre des intervalles d’Allen pour raisonner sur des intervalles de temps certains et incertains. Une adaptation de relations cette algèbre pour relier un intervalle de temps et un point de temps, et deux points de temps. (3) Enfin une ontologie qui intègre toutes ces extensions. Un prototype a été implémenté et intégré dans une prothèse de mémoire pour les patients atteints de la maladie d’Alzheimer afin de gérer des entrées de données incertaines.

Dealing with imperfect temporal data entries in the context of Collective and Personal Memory applications is an imperative matter. Data are structured semantically using an ontology called “Collective Memo Onto”. In this paper, we propose an approach that handles temporal data imperfections in OWL 2. We reduce to four types of imperfection defined in our typology of temporal data imperfections which are imprecision, uncertainty, simultaneously uncertainty and imprecision and conflict. The approach consists of representing imperfect quantitative and qualitative time intervals and time points by extending the 4D-fluents approach and defining new components, as well as reasoning about the handled data by extending the Allen’s Interval algebra. Based on both extensions, we propose an OWL 2 ontology named “TimeOntoImperfection”. The proposed qualitative temporal relations are inferred via a set of 924 SWRL rules. We validate our work by implementing a prototype based on the proposed ontology and we apply it in the context of the Collective Memory Temporal Data.

In this paper, we propose an approach to handling uncertain time intervals and related qualitative relations, based on possibility theory. Four contributions are included in this approach. (1) Representing uncertain time intervals and related qualitative relations by extending the 4D-fluents approach with new ontological components. (2) Reasoning about uncertain time intervals by extending the Allen’s interval algebra. The resulting interval relations preserve the good properties of the original algebra. (3) Proposing an OWL 2 possibilistic temporal ontology based on 4D-fluents approach extension and Allen’s interval algebra extension. The proposed qualitative temporal relations are inferred via a set of SWRL rules. We validate our work by implementing a prototype based on this ontology. (4) Applying our work to PersonLink ontology and comparing the obtained results with our previous works.

Temporal data given by Alzheimer's patients are mostly uncertain. Many approaches have been proposed to handle certain temporal data and lack uncertain ones. This paper proposes an approach to represent and reason about quantitative time intervals and points and qualitative relations between them. It is suitable to handle certain and uncertain temporal data. It includes three parts. (1) The authors extend the 4D-fluents approach with certain components to represent certain and uncertain temporal data. (2) They extend the Allen's interval algebra to reason about certain and uncertain time intervals. They adapt these relations to relate a time interval and a time point, and two time points. All relations can be used for temporal reasoning by means of transitivity tables. (3) They propose a certain ontology based on the extensions. A prototype is implemented and integrated into an ontology-based memory prosthesis for Alzheimer's patients to handle uncertain data inputs. The evaluation proves the usefulness of the approach as all the inferences are well established and the precision results are promising.