Title:	Ontology learning for facilitating ontology matching in automotive
Authors:	Šebek, Ondrej Jirkovský, Václav Rychtyckyj, Nestor Kadera, Petr
Citation:	STEINBERGER, Josef ed.; ZÍMA, Martin ed.; FIALA, Dalibor ed.; DOSTAL, Martin ed.; NYKL, Michal ed. Data a znalosti 2017: sborník konference, Plzeň, Hotel Angelo 5. - 6. října 2017. 1. vyd. Plzeň: Západočeská univerzita v Plzni, 2017, s. 31-34. ISBN 978-80-261-0720-0.
Issue Date:	2017
Publisher:	Západočeská univerzita v Plzni
Document type:	konferenční příspěvek conferenceObject
URI:	https://www.zcu.cz/export/sites/zcu/pracoviste/vyd/online/DataAZnalosti2017.pdf http://hdl.handle.net/11025/26331
ISBN:	978-80-261-0720-0
Keywords:	ontologie;heterogenita;matching
Keywords in different language:	ontology;heterogeneity;matching
Abstract in different language:	All manufacturing companies need to monitor a large number of de-vices and from which critical data must be captured and analyzed. The increas-ing complexity of these ecosystems emphasizes the requirement for a flexible and versatile data model architecture. Ontologies may facilitate a proper under-standing of the problem domain as well as the interoperability with surrounding systems using ontology matching approach. However, data models of surround-ing systems are not always ontologies. Thus, concepts and relations among them have to be extracted from the models to enable their integration with the ontology. The definition of concepts, their hierarchy, relations between con-cepts, and properties from a general architecture is a complex task and has to be tailored to an application’s needs. In this paper, we propose an involvement of the ontology learning approach to the process of ontology matching in the au-tomotive.
Rights:	© Západočeská univerzita v Plzni
Appears in Collections:	Data a znalosti 2017 Data a znalosti 2017

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.