TY - GEN
T1 - A Knowledge Management Approach Supporting Model-Based Systems Engineering
AU - Yang, Pengfei
AU - Lu, Jinzhi
AU - Feng, Lei
AU - Wu, Shouxuan
AU - Wang, Guoxin
AU - Kiritsis, Dimitris
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Model-based Systems Engineering (MBSE) is a noval approach to support complex system development by formalizing system artifacts and development using models. Though MBSE models provide a completely structural formalisms about system development for system developers, such large of domain specific knowledge represented by models cannot be captured as what the developers expect. This leads to a big challenge when MBSE can be widely used for complex system development. In this paper, a knowledge management approach is proposed to support an intelligent question answering scenario when implementing MBSE in system lifecycle. We make use of the GOPPRR approach to support MBSE formalisms which are transformed to knowledge graph models. Then such models provide cues for intelligent question answers through reasoning. In the case study, we make use of an auto-braking system scenario to develop MBSE models and to implement the intelligent question answering. Finally, we find the availability of our approach is evaluated which the domain engineers enable to capture their domain knowledge more efficiently.
AB - Model-based Systems Engineering (MBSE) is a noval approach to support complex system development by formalizing system artifacts and development using models. Though MBSE models provide a completely structural formalisms about system development for system developers, such large of domain specific knowledge represented by models cannot be captured as what the developers expect. This leads to a big challenge when MBSE can be widely used for complex system development. In this paper, a knowledge management approach is proposed to support an intelligent question answering scenario when implementing MBSE in system lifecycle. We make use of the GOPPRR approach to support MBSE formalisms which are transformed to knowledge graph models. Then such models provide cues for intelligent question answers through reasoning. In the case study, we make use of an auto-braking system scenario to develop MBSE models and to implement the intelligent question answering. Finally, we find the availability of our approach is evaluated which the domain engineers enable to capture their domain knowledge more efficiently.
KW - Knowledge graph modeling
KW - Knowledge management
KW - Knowledge reasoning
KW - Model-based system engineering
UR - http://www.scopus.com/inward/record.url?scp=85107325246&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-72651-5_55
DO - 10.1007/978-3-030-72651-5_55
M3 - Conference contribution
AN - SCOPUS:85107325246
SN - 9783030726508
T3 - Advances in Intelligent Systems and Computing
SP - 581
EP - 590
BT - Trends and Applications in Information Systems and Technologies
A2 - Rocha, Álvaro
A2 - Adeli, Hojjat
A2 - Dzemyda, Gintautas
A2 - Moreira, Fernando
A2 - Correia, Ana Maria Ramalho
PB - Springer Science and Business Media Deutschland GmbH
T2 - World Conference on Information Systems and Technologies, WorldCIST 2021
Y2 - 30 March 2021 through 2 April 2021
ER -
