A Knowledge Graph-Driven Approach for Architecture Design Space Generation and Reduction

Yanshao Sun; Ru Wang; Yu Huang; Zhendong Liu

doi:10.1007/978-981-97-0922-9_9

A Knowledge Graph-Driven Approach for Architecture Design Space Generation and Reduction

Yanshao Sun, Ru Wang^*, Yu Huang, Zhendong Liu

^*Corresponding author for this work

School of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The process of architecture design for complex systems relies on domain knowledge, which involves identifying architecture decision issues from text-based requirements and generating the architecture design space. As the complexity of the system increases, higher requirement is proposed for the storage and reuse of design knowledge. To address this challenge, this paper proposes a knowledge graph-driven approach for architecture design space generating and reduction. To represent and store knowledge in architecture decisions, a decision-oriented design knowledge graph (DDKG) is constructed by analyzing the relationship among requirements, functions, and knowledge in architecture decisions. The nodes in DDKG to generate the architecture design space are matched with decision issue keywords extracted from the user’s requirement text. Moreover, the design space is generated from nodes matched in DDKG and reduced by forming a Constraint Satisfaction Problem (CSP) model. The method proposed in this paper has been validated in the generation and reduction of architectural design space for the first-stage separation systems of launch vehicles.

Original language	English
Title of host publication	Advances in Mechanical Design - The Proceedings of the 2023 International Conference on Mechanical Design, ICMD 2023
Editors	Jianrong Tan, Yu Liu, Hong-Zhong Huang, Jingjun Yu, Zequn Wang
Publisher	Springer Science and Business Media B.V.
Pages	129-143
Number of pages	15
ISBN (Print)	9789819709212
DOIs	https://doi.org/10.1007/978-981-97-0922-9_9
Publication status	Published - 2024
Event	International Conference on Mechanical Design, ICMD 2023 - Chengdu, China Duration: 20 Oct 2023 → 22 Oct 2023

Publication series

Name	Mechanisms and Machine Science
Volume	155 MMS
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	International Conference on Mechanical Design, ICMD 2023
Country/Territory	China
City	Chengdu
Period	20/10/23 → 22/10/23

Keywords

Architecture design space
Conceptual design
Knowledge graph

Access to Document

10.1007/978-981-97-0922-9_9

Cite this

Sun, Y., Wang, R., Huang, Y., & Liu, Z. (2024). A Knowledge Graph-Driven Approach for Architecture Design Space Generation and Reduction. In J. Tan, Y. Liu, H.-Z. Huang, J. Yu, & Z. Wang (Eds.), Advances in Mechanical Design - The Proceedings of the 2023 International Conference on Mechanical Design, ICMD 2023 (pp. 129-143). (Mechanisms and Machine Science; Vol. 155 MMS). Springer Science and Business Media B.V.. https://doi.org/10.1007/978-981-97-0922-9_9

Sun, Yanshao ; Wang, Ru ; Huang, Yu et al. / A Knowledge Graph-Driven Approach for Architecture Design Space Generation and Reduction. Advances in Mechanical Design - The Proceedings of the 2023 International Conference on Mechanical Design, ICMD 2023. editor / Jianrong Tan ; Yu Liu ; Hong-Zhong Huang ; Jingjun Yu ; Zequn Wang. Springer Science and Business Media B.V., 2024. pp. 129-143 (Mechanisms and Machine Science).

@inproceedings{fb665ffb8ebd44d1bd759e1dc3596f90,

title = "A Knowledge Graph-Driven Approach for Architecture Design Space Generation and Reduction",

abstract = "The process of architecture design for complex systems relies on domain knowledge, which involves identifying architecture decision issues from text-based requirements and generating the architecture design space. As the complexity of the system increases, higher requirement is proposed for the storage and reuse of design knowledge. To address this challenge, this paper proposes a knowledge graph-driven approach for architecture design space generating and reduction. To represent and store knowledge in architecture decisions, a decision-oriented design knowledge graph (DDKG) is constructed by analyzing the relationship among requirements, functions, and knowledge in architecture decisions. The nodes in DDKG to generate the architecture design space are matched with decision issue keywords extracted from the user{\textquoteright}s requirement text. Moreover, the design space is generated from nodes matched in DDKG and reduced by forming a Constraint Satisfaction Problem (CSP) model. The method proposed in this paper has been validated in the generation and reduction of architectural design space for the first-stage separation systems of launch vehicles.",

keywords = "Architecture design space, Conceptual design, Knowledge graph",

author = "Yanshao Sun and Ru Wang and Yu Huang and Zhendong Liu",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; International Conference on Mechanical Design, ICMD 2023 ; Conference date: 20-10-2023 Through 22-10-2023",

year = "2024",

doi = "10.1007/978-981-97-0922-9_9",

language = "English",

isbn = "9789819709212",

series = "Mechanisms and Machine Science",

publisher = "Springer Science and Business Media B.V.",

pages = "129--143",

editor = "Jianrong Tan and Yu Liu and Hong-Zhong Huang and Jingjun Yu and Zequn Wang",

booktitle = "Advances in Mechanical Design - The Proceedings of the 2023 International Conference on Mechanical Design, ICMD 2023",

address = "Germany",

}

Sun, Y, Wang, R, Huang, Y & Liu, Z 2024, A Knowledge Graph-Driven Approach for Architecture Design Space Generation and Reduction. in J Tan, Y Liu, H-Z Huang, J Yu & Z Wang (eds), Advances in Mechanical Design - The Proceedings of the 2023 International Conference on Mechanical Design, ICMD 2023. Mechanisms and Machine Science, vol. 155 MMS, Springer Science and Business Media B.V., pp. 129-143, International Conference on Mechanical Design, ICMD 2023, Chengdu, China, 20/10/23. https://doi.org/10.1007/978-981-97-0922-9_9

A Knowledge Graph-Driven Approach for Architecture Design Space Generation and Reduction. / Sun, Yanshao; Wang, Ru; Huang, Yu et al.
Advances in Mechanical Design - The Proceedings of the 2023 International Conference on Mechanical Design, ICMD 2023. ed. / Jianrong Tan; Yu Liu; Hong-Zhong Huang; Jingjun Yu; Zequn Wang. Springer Science and Business Media B.V., 2024. p. 129-143 (Mechanisms and Machine Science; Vol. 155 MMS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Knowledge Graph-Driven Approach for Architecture Design Space Generation and Reduction

AU - Sun, Yanshao

AU - Wang, Ru

AU - Huang, Yu

AU - Liu, Zhendong

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

PY - 2024

Y1 - 2024

N2 - The process of architecture design for complex systems relies on domain knowledge, which involves identifying architecture decision issues from text-based requirements and generating the architecture design space. As the complexity of the system increases, higher requirement is proposed for the storage and reuse of design knowledge. To address this challenge, this paper proposes a knowledge graph-driven approach for architecture design space generating and reduction. To represent and store knowledge in architecture decisions, a decision-oriented design knowledge graph (DDKG) is constructed by analyzing the relationship among requirements, functions, and knowledge in architecture decisions. The nodes in DDKG to generate the architecture design space are matched with decision issue keywords extracted from the user’s requirement text. Moreover, the design space is generated from nodes matched in DDKG and reduced by forming a Constraint Satisfaction Problem (CSP) model. The method proposed in this paper has been validated in the generation and reduction of architectural design space for the first-stage separation systems of launch vehicles.

AB - The process of architecture design for complex systems relies on domain knowledge, which involves identifying architecture decision issues from text-based requirements and generating the architecture design space. As the complexity of the system increases, higher requirement is proposed for the storage and reuse of design knowledge. To address this challenge, this paper proposes a knowledge graph-driven approach for architecture design space generating and reduction. To represent and store knowledge in architecture decisions, a decision-oriented design knowledge graph (DDKG) is constructed by analyzing the relationship among requirements, functions, and knowledge in architecture decisions. The nodes in DDKG to generate the architecture design space are matched with decision issue keywords extracted from the user’s requirement text. Moreover, the design space is generated from nodes matched in DDKG and reduced by forming a Constraint Satisfaction Problem (CSP) model. The method proposed in this paper has been validated in the generation and reduction of architectural design space for the first-stage separation systems of launch vehicles.

KW - Architecture design space

KW - Conceptual design

KW - Knowledge graph

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U2 - 10.1007/978-981-97-0922-9_9

DO - 10.1007/978-981-97-0922-9_9

M3 - Conference contribution

AN - SCOPUS:85199282857

SN - 9789819709212

T3 - Mechanisms and Machine Science

SP - 129

EP - 143

BT - Advances in Mechanical Design - The Proceedings of the 2023 International Conference on Mechanical Design, ICMD 2023

A2 - Tan, Jianrong

A2 - Liu, Yu

A2 - Huang, Hong-Zhong

A2 - Yu, Jingjun

A2 - Wang, Zequn

PB - Springer Science and Business Media B.V.

T2 - International Conference on Mechanical Design, ICMD 2023

Y2 - 20 October 2023 through 22 October 2023

ER -

Sun Y, Wang R, Huang Y, Liu Z. A Knowledge Graph-Driven Approach for Architecture Design Space Generation and Reduction. In Tan J, Liu Y, Huang HZ, Yu J, Wang Z, editors, Advances in Mechanical Design - The Proceedings of the 2023 International Conference on Mechanical Design, ICMD 2023. Springer Science and Business Media B.V. 2024. p. 129-143. (Mechanisms and Machine Science). doi: 10.1007/978-981-97-0922-9_9

A Knowledge Graph-Driven Approach for Architecture Design Space Generation and Reduction

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