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Certain and Uncertain Temporal Data Representation and Reasoning in OWL 2

Certain and Uncertain Temporal Data Representation and Reasoning in OWL 2

Nassira Achich, Fatma Ghorbel, Fayçal Hamdi, Elisabeth Métais, Faiez Gargouri
Copyright: © 2021 |Volume: 17 |Issue: 3 |Pages: 22
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799859727|DOI: 10.4018/IJSWIS.2021070104
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MLA

Achich, Nassira, et al. "Certain and Uncertain Temporal Data Representation and Reasoning in OWL 2." IJSWIS vol.17, no.3 2021: pp.51-72. http://doi.org/10.4018/IJSWIS.2021070104

APA

Achich, N., Ghorbel, F., Hamdi, F., Métais, E., & Gargouri, F. (2021). Certain and Uncertain Temporal Data Representation and Reasoning in OWL 2. International Journal on Semantic Web and Information Systems (IJSWIS), 17(3), 51-72. http://doi.org/10.4018/IJSWIS.2021070104

Chicago

Achich, Nassira, et al. "Certain and Uncertain Temporal Data Representation and Reasoning in OWL 2," International Journal on Semantic Web and Information Systems (IJSWIS) 17, no.3: 51-72. http://doi.org/10.4018/IJSWIS.2021070104

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Abstract

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.

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