Authors:
Nassira Achich
1
;
Fatma Ghorbel
2
;
Fayçal Hamdi
3
;
Elisabeth Metais
3
and
Faiez Gargouri
1
Affiliations:
1
MIRACL Laboratory, University of Sfax and Tunisia
;
2
MIRACL Laboratory, University of Sfax, Tunisia, CEDRIC Laboratory, Conservatoire National des Arts et Métiers (CNAM) and France
;
3
CEDRIC Laboratory, Conservatoire National des Arts et Métiers (CNAM) and France
Keyword(s):
Data Imperfections, Typology of Temporal Data Imperfection, Direct and Indirect Imperfections, Natural Language based Temporal Data, Context - Dependent Temporal Data.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Collaboration and e-Services
;
e-Business
;
Enterprise Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Semantic Web
;
Soft Computing
;
Symbolic Systems
Abstract:
Temporal data may be subject to several types of imperfection (e.g., uncertainty, imprecision..). In this context, several typologies of data imperfections have been already proposed. However, these typologies cannot be applied to temporal data because of the complexity of this type of data and the specificity that it contains. Besides, to the best of our knowledge, there is no typology of temporal data imperfections. In this paper, we propose a typology of temporal data imperfections. Our typology is divided into direct imperfections of both numeric temporal data and natural language based temporal data, indirect imperfections that can be deduced from the direct ones and granularity (i.e., context - dependent temporal data) which is related to several factors that can interfer in specifying the imperfection type such as person’s profile and multiculturalism. We finish by representing an example of imprecise temporal data in PersonLink ontology.