Abstract
Temporal data may be precise or imprecise. Representing and reasoning about these kinds of data in ontology still needs to be addressed. A significant number of approaches exist. However, they handle only precise temporal data and lack imprecise ones. In this paper, we propose a crisp-based approach for representing and reasoning about temporal data in term of quantitative (i.e., time points that can be dates and clocks, and time intervals) as well as qualitative relations (e.g., “before”) in ontology. It aims to support not only precise time points and intervals, but also imprecise ones e.g., “The journey starts by the beginning of June and ends by mid-June”. It relies only on crisp exiting Semantic Web standards and it is modeled in crisp ontology. Our approach is based on three blocks. (i) We extend the 4D-fluents approach with new crisp ontological components to represent the mentioned precise and imprecise temporal data. (ii) We extend the Allen’s interval algebra to reason about imprecise time intervals. Compared to related work, our extension is entirely based on crisp set theory. The resulting interval relations preserve many of the desirable properties of the original algebra. We adapt these relations to allow relating a time interval and a time point, and two time points; where time points and intervals may be both precise or both imprecise. All proposed relations can be used for temporal reasoning by means of transitivity tables. (iii) We propose an OWL 2 ontology based on our extensions. It proposes a set of SWRL rules to infer the proposed qualitative temporal relations. A prototype based on this ontology is implemented. We apply our approach to the Travel ontology.
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Achich, N., Ghorbel, F., Hamdi, F., Metais, E., Gargouri, F. (2019). Representing and Reasoning About Precise and Imprecise Time Points and Intervals in Semantic Web: Dealing with Dates and Time Clocks. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science(), vol 11707. Springer, Cham. https://doi.org/10.1007/978-3-030-27618-8_15
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