A Trajectory Ontology Design Pattern for Semantic Trajectory Data Warehouses

Author(s):  
Marwa Manaa ◽  
Thouraya Sakouhi ◽  
Jalel Akaichi

Mobility data became an important paradigm for computing performed in various areas. Mobility data is considered as a core revealing the trace of mobile objects displacements. While each area presents a different optic of trajectory, they aim to support mobility data with domain knowledge. Semantic annotations may offer a common model for trajectories. Ontology design patterns seem to be promising solutions to define such trajectory related pattern. They appear more suitable for the annotation of multiperspective data than the only use of ontologies. The trajectory ontology design pattern will be used as a semantic layer for trajectory data warehouses for the sake of analyzing instantaneous behaviors conducted by mobile entities. In this chapter, the authors propose a semantic approach for the semantic modeling of trajectory and trajectory data warehouses based on a trajectory ontology design pattern. They validate the proposal through real case studies dealing with behavior analysis and animal tracking case studies.

Author(s):  
Valentina Anita Carriero ◽  
Aldo Gangemi ◽  
Andrea Giovanni Nuzzolese ◽  
Valentina Presutti

Author(s):  
Yingjie Hu ◽  
Krzysztof Janowicz ◽  
David Carral ◽  
Simon Scheider ◽  
Werner Kuhn ◽  
...  

Author(s):  
Gaurav Sinha ◽  
David Mark ◽  
Dave Kolas ◽  
Dalia Varanka ◽  
Boleslo E. Romero ◽  
...  

2017 ◽  
Vol 10 (2) ◽  
pp. 59 ◽  
Author(s):  
Denis Eka Cahyani ◽  
Ito Wasito

An ontology is defined as an explicit specification of a conceptualization, which is an important tool for modeling, sharing and reuse of domain knowledge. However, ontology construction by hand is a complex and a time consuming task. This research presents a fully automatic method to build bilingual domain ontology from text corpora and ontology design patterns (ODPs) in Alzheimer’s disease. This method combines two approaches: ontology learning from texts and matching with ODPs. It consists of six steps: (i) Term & relation extraction (ii) Matching with Alzheimer glossary (iii) Matching with ontology design patterns (iv) Score computation similarity term & relation with ODPs (v) Ontology building (vi) Ontology evaluation. The result of ontology composed of 381 terms and 184 relations with 200 new terms and 42 new relations were added. Fully automatic ontology construction has higher complexity, shorter time and reduces role of the expert knowledge to evaluate ontology than manual ontology construction. This proposed method is sufficiently flexible to be applied to other domains.


Author(s):  
David Carral ◽  
Simon Scheider ◽  
Krzysztof Janowicz ◽  
Charles Vardeman ◽  
Adila A. Krisnadhi ◽  
...  

2012 ◽  
Vol 3 (Suppl 2) ◽  
pp. S2 ◽  
Author(s):  
Djamila Seddig-Raufie ◽  
Ludger Jansen ◽  
Daniel Schober ◽  
Martin Boeker ◽  
Niels Grewe ◽  
...  

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