Dealing with Precise and Imprecise Temporal Data in Crisp Ontology

Author:

Ghorbel Fatma1,Hamdi Fayçal2ORCID,Métais Elisabeth3

Affiliation:

1. University of Sfax, Sfax, Tunisia

2. CEDRIC - Conservatoire National des Arts et Métiers, Paris, France

3. CEDRIC Laboratory, Paris, France

Abstract

This article proposes a crisp-based approach for representing and reasoning about concepts evolving in time and of their properties in terms of qualitative relations (e.g., “before”) in addition to quantitative ones, time intervals and points. It is not only suitable to handle precise time intervals and points, but also imprecise ones. It extends the 4D-fluents approach with crisp components to represent handed data. It also extends the Allen's interval algebra. This extension allows reasoning about imprecise time intervals. Compared to related work, it is based on crisp set theory. These relations preserve many properties of the original algebra. Their definitions are adapted to allow relating a time interval and a time point, and two time points. All relations can be used for temporal reasoning by means of transitivity tables. Finally, it proposes a crisp ontology that based on the extended Allen's algebra instantiates the 4D-fluents-based representation.

Publisher

IGI Global

Subject

General Computer Science

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Ontology-based soft computing and machine learning model for efficient retrieval;Knowledge and Information Systems;2023-10-09

2. Handling Temporal Data Imperfections in OWL 2 - Application to Collective Memory Data Entries;Research Challenges in Information Science;2022

3. Handling Uncertain Time Intervals in OWL 2: Possibility Vs Probability Theories-based Approaches;2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE);2021-07-11

4. Certain and Uncertain Temporal Data Representation and Reasoning in OWL 2;International Journal on Semantic Web and Information Systems;2021-07

5. Diagramming Imprecise and Incomplete Temporal Information;Diagrammatic Representation and Inference;2021

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