Authors:
Hadhami Ounissi
1
;
Marwa Manaa
1
and
Jalel Akaichi
2
Affiliations:
1
Université de Tunis, ISG and BESTMOD, Tunisia
;
2
College of Computer Science and King Khaled University, Saudi Arabia
Keyword(s):
Human Mobility, Ontology, Semantic Modeling, Social Networks, Spatial Data, Trajectory Data.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Engineering
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Ontology Sharing and Reuse
;
Symbolic Systems
Abstract:
The volume of data keeps growing rapidly, especially with the arrival and the frequent access to social
networks. The spread of these networks provides users the opportunity to share their social, geographical and
temporal information through geo-localized tweets and check-ins. The challenge is to exploit these data leads
to a decision in favour of different situations encountered by these users. Thus, if we successfully analyze
their trends according to the models of users’ movements, we can then draw conclusions about the evolution
of their instantaneous behavior and accomplished activities. But, the problem is that the use of such data
decrees the provision of a representative formalism that combines spatial data and user information. In this
paper, we propose an approach for a semantic modeling of social network users’ trajectories. To do so,
ontology seems to be a promising solution that allows us to annotate raw trajectories with semantic
information to give birth to semantic t
rajectories. Such semantic trajectories are then analyzed in order to
detect user behavior in a dynamic way.
(More)