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Contributions to Books:

R. Sabou, O. Kovalenko, P. Novak:
"Semantic Modelling and Acquisition of Engineering Knowledge";
in: "Semantic Web for Intelligent Engineering Applications", Springer International Publishing Switzerland, 2016, ISBN: 978-3-319-41488-1, 105 - 136.



English abstract:
Ontologies are key Semantic Web technologies (SWTs) that provide
means to formally and explicitly represent domain knowledge in terms of key domain
concepts and their relations. Therefore, the creation of intelligent engineering
applications (IEAs) that rely on SWTs depends on the creation of a suitable ontology
that semantically models engineering knowledge and the representation of engineering
data in terms of this ontology (i.e., through a knowledge acquisition process).
The tasks of semantic modelling and acquisition of engineering knowledge
are, however, complex tasks that rely on specialized skills provided by a knowledge
engineer and can therefore be daunting for those SWT adopters that do not possess
this skill set. This chapter aims to support these SWT adopters by summing up essential
knowledge for creating and populating ontologies including: ontology engineering
methodologies and methods for assessing the quality of the created ontologies.
The chapter provides examples of concrete engineering ontologies, and
classifies these engineering ontologies in a framework based on the Product-Process-
Resource abstraction. The chapter also contains examples of best practices for
modelling common situations in the engineering domain using ontology design patterns,
and gives an overview of the current tools that engineers can use to lift engineering
data stored in legacy formats (such as, spreadsheets, XML files, and databases,
etc.) to a semantic representation.

Keywords:
ontology modelling, ontology engineering methodologies, ontology evaluation, classification of engineering ontologies, ontology design patterns, ontology population


"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1007/978-3-319-41490-4


Created from the Publication Database of the Vienna University of Technology.