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ity and correctness are the two important aspects con-
cerning heavyweight ontology evaluation. With these
perspectives in mind, we identified different metrics
to measure the ontology quality (computational effi-
ciency, adaptability and clarity) and the ontology cor-
rectness (accuracy, completeness, clarity, and consis-
tency) (Hlomani and Stacey, 2014).
Modularity also allows the re-usability of the on-
tology or of its modules. Indeed, we proposed a
framework to guarantee the safety of the autonomous
system from the upstream design phases thanks to the
use of ATMO and its alignment with the safety rules
(Chouchani et al., 2022).
5 CONCLUSIONS AND FUTURE
WORKS
In this work, we presented the methodological frame-
work adopted to develop ATMO the modular ontology
of on-board map of autonomous train. The main con-
tributions of this work are : (i) the use of standards
to provide a semantic map model ; (ii) the detailed
description of the ontology development methodol-
ogy METHONTOLOGY ; and (iii) the modularization
paradigm used to manage the complexity of the on-
tology.
knowledge acquisitions were explicit and implicit
by referring to bibliographic research, expert opinions
as well as national and international standards and
models. After a validation of the resulted lightweight
ontology, presented in the form of the UML model,
the ontology is sufficient enough to experiment with
reasoning and evaluate the heavyweight ontology.
The problem of building a modular ontology ap-
proached in this work, can serve as a basis for a reflec-
tion on the approach of developing an ontology of the
railway domain in general. Indeed, this proposal will
be discussed in future work within the framework of
the OntoRail project aiming to create a global dictio-
nary and to unify the vocabulary used by the various
international actors and standards like RSM, IFC and
EULYNX (OntoRail, 2022).
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