How to Understand Better “Smart Vehicle”? Knowledge Extraction …
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Table 1 Ontology-based IoT automotive projects used in the dataset that we analyzed
Authors
Year Expertise
OA
Reasoning
Klotz et al. [2, 4] [5]
2018 BMW: vehicle signal and
attribute
OWL restriction
OpenSensingCity
2018 Parking scenario
×
Bike scenario
×
CityPulse [11, 12]
2016 Traffic analysis scenario
–
Gyrard et al. [3]
2014 Transport ontology
Jena rules
BMW summer school
Morignot et al. [13]
2013 Autonomous vehicle
assistance
foggy -> mode manual
Zhao et al. [1, 14–16]
2015 Toyota: safe autonomous
driving
–
Lecue et al. [17]
2014 STAR-CITY: transport
ontology
–
Ruta et al. [18–20]
2017 iDriveSafe ontology
OWL restrictions
Mafalda projet (3
ontologies)
Maarala [21]
2017 Traffic ontology
16 rules, OWL
restrictions
Bermejo et al. [22]
2013 Road traffic management
ontology
77 rules/actions
(SWRL DLSafe rule in
the ontology)
Dardailler et al.
2012 W3C road accident
ontology
×
Corsar et al. [23]
2015 Transport disruption
ontology
OWL restriction
Codescu et al. [24]
2011 Open street map and route
planning
×
Grausberg, Fuchs et al.
[25, 26]
2008 Driver assistance system
ontology
OWL restriction (rule
speed max min)
Hepp et al.
–
W3C vehicle sales ontology
× No owl: restriction
Legend Ontology Availability (OA)
Other projects related to the topic that cannot be used since ontologies are not shared
(as depicted in Table 2). Although scientific publications were really interesting,
those ontologies have been discarded since we cannot find their ontology online
(see Table 2).
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