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Schematic of DRMS-based conceptual design

Graphical Abstract Figure

Schematic of DRMS-based conceptual design

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Abstract

Design rationale (DR) explains why the solution is designed the way it is, and can be used to stimulate creativity and facilitate the development of new solutions in the conceptual design phase. DR was mainly captured by recording the tacit knowledge of designers during the design process, which has hindered its application in conceptual design due to its interference with the design. This paper proposes a method for capturing DR from technical literature, providing an intuitively understandable textual stimulus for design ideation. A textual DR ontology, which includes literature, artifact, issue, intention, argument, and other entities along with their relationships, is used as a metamodel to construct the DR knowledge graph (DRKG). The DR vector space (DRVS) model and the DRVS-based method are used for the joint extraction of entities and relations. Sentences and terms extracted from the technical literature are then organized into a DRKG. A prototype design rationale management system was developed based on the methodology. Finally, we carried out experiments to construct the DRKG and apply it to the conceptual design of a police unmanned aerial vehicle for night patrols using patents and journal articles, and the results verified the feasibility of the method.

References

1.
Kodama
,
M.
,
2019
, “
Boundaries Knowledge (Knowing)—A Source of Business Innovation
,”
Knowl. Process Manage.
,
26
(
3
), pp.
210
228
.
2.
Siddharth
,
L.
, and
Sarkar
,
P.
,
2018
, “
A Multiple-Domain Matrix Support to Capture Rationale for Engineering Design Changes
,”
ASME J. Comput. Inf. Sci. Eng.
,
18
(
2
), p.
021014
.
3.
Goldschmidt
,
G.
, and
Sever
,
A. L.
,
2011
, “
Inspiring Design Ideas With Texts
,”
Des. Stud.
,
32
(
2
), pp.
139
155
.
4.
Jin
,
Y.
, and
Zhang
,
Z.
,
2022
, “
Data-Enabled Sketch Search and Retrieval for Visual Design Stimuli Generation
,”
Artif. Intell. Eng. Des., Anal. Manuf.
,
36
, p.
e25
.
5.
Liu
,
L.
,
Li
,
Y.
,
Xiong
,
Y.
, and
Cavallucci
,
D.
,
2020
, “
A New Function-Based Patent Knowledge Retrieval Tool for Conceptual Design of Innovative Products
,”
Comput. Ind.
,
115
, p.
103154
.
6.
Valverde
,
U. Y.
,
Nadeau
,
J.-P.
, and
Scaravetti
,
D.
,
2017
, “
A New Method for Extracting Knowledge From Patents to Inspire Designers During the Problem-Solving Phase
,”
J. Eng. Des.
,
28
(
6
), pp.
369
407
.
7.
Jiang
,
Z.
,
Ma
,
Y.
, and
Xiong
,
Y.
,
2023
, “
Bio-Inspired Generative Design for Engineering Products: A Case Study for Flapping Wing Shape Exploration
,”
Adv. Eng. Inform.
,
58
, p.
102240
.
8.
Huang
,
Z.
,
Guo
,
X.
,
Liu
,
Y.
,
Zhao
,
W.
, and
Zhang
,
K.
,
2023
, “
A Smart Conflict Resolution Model Using Multi-layer Knowledge Graph for Conceptual Design
,”
Adv. Eng. Inform.
,
55
, p.
101887
.
9.
Yu
,
H.
,
Zhao
,
W.
, and
Zhao
,
Q.
,
2022
, “
Distributed Representation Learning and Intelligent Retrieval of Knowledge Concepts for Conceptual Design
,”
Adv. Eng. Inform.
,
53
, p.
101649
.
10.
Liu
,
Q.
,
Wang
,
K.
,
Li
,
Y.
,
Chen
,
C.
, and
Li
,
W.
,
2022
, “
A Novel Function-Structure Concept Network Construction and Analysis Method for a Smart Product Design System
,”
Adv. Eng. Inform.
,
51
, p.
101502
.
11.
Thomas
,
R. G.
, and
Daniel
,
R.
,
1991
,
Design Knowledge and Design Rationale: A Framework for Representation, Capture, and Use
,
KSL 90-45, Knowledge Systems Laboratory, Computer Science Department, Stanford University
,
Stanford, CA
.
12.
Lee
,
J.
,
1989
, Decision Representation Language (DRL) and Its Support Environment. Available: http://hdl.handle.net/1721.1/41499. Accessed September 7, 2022.
13.
Yue
,
G.
,
Liu
,
J.
, and
Hou
,
Y.
,
2018
,
Design Rationale Knowledge Management: A Survey
,
Y.
Luo
, ed.,
Springer International Publishing
,
Cham, Switzerland
, pp.
245
253
.
14.
Liu
,
Y.
,
Liang
,
Y.
,
Kwong
,
C. K.
, and
Lee
,
W. B.
,
2010
, “
A New Design Rationale Representation Model for Rationale Mining
,”
ASME J. Comput. Inf. Sci. Eng.
,
10
(
3
), p.
031009
.
15.
Liang
,
Y.
,
Liu
,
Y.
,
Kwong
,
C. K.
, and
Lee
,
W. B.
,
2012
, “
Learning the ‘Whys’: Discovering Design Rationale Using Text Mining—An Algorithm Perspective
,”
Comput. Aided Des.
,
44
(
10
), pp.
916
930
.
16.
Lester
,
M.
,
Guerrero
,
M.
, and
Burge
,
J.
,
2020
, “
Using Evolutionary Algorithms to Select Text Features for Mining Design Rationale
,”
Artif. Intell. Eng. Des., Anal. Manuf.
,
34
(
2
), pp.
132
146
.
17.
Kurtanović
,
Z.
, and
Maalej
,
W.
,
2018
, “
On User Rationale in Software Engineering
,”
Requir. Eng.
,
23
(
3
), pp.
357
379
.
18.
Lou
,
S.
,
Feng
,
Y.
,
Gao
,
Y.
,
Zheng
,
H.
,
Peng
,
T.
, and
Tan
,
J.
,
2023
, “
A Function-Behavior Mapping Approach for Product Conceptual Design Inspired by Memory Mechanism
,”
Adv. Eng. Inform.
,
58
, p.
102236
.
19.
Gonçalves
,
M.
,
Cardoso
,
C.
, and
Badke-Schaub
,
P.
,
2014
, “
What Inspires Designers? Preferences on Inspirational Approaches During Idea Generation
,”
Des. Stud.
,
35
(
1
), pp.
29
53
.
20.
Goucher-Lambert
,
K.
,
Huang
,
F.
, and
Kwon
,
E.
,
2022
, “
Enabling Multi-modal Search for Inspirational Design Stimuli Using Deep Learning
,”
Artif. Intell. Eng. Des., Anal. Manuf.
,
36
, p.
e22
.
21.
Luo
,
J.
,
Song
,
B.
, and
Srinivasan
,
V.
,
2017
, “
Patent Stimuli Search and Its Influence on Ideation Outcomes
,”
Des. Sci.
,
3
, p.
e25
.
22.
Han
,
J.
, and
Wang
,
D.
,
2023
, “
Exploring the Impact of Generative Stimuli on the Creativity of Designers in Combinational Design
,”
Proc. Des. Soc.
,
3
, pp.
1805
1814
.
23.
Kwon
,
E.
,
Rao
,
V.
, and
Goucher-Lambert
,
K.
,
2023
, “
Understanding Inspiration: Insights Into How Designers Discover Inspirational Stimuli Using an AI-Enabled Platform
,”
Des. Stud.
,
88
, p.
101202
.
24.
Kotecha
,
M. C.
,
Chen
,
T.-J.
,
McAdams
,
D. A.
, and
Krishnamurthy
,
V.
,
2021
, “
Design Ideation Through Speculative Fiction: Foundational Principles and Exploratory Study
,”
ASME J. Mech. Des.
,
143
(
8
), p.
080801
.
25.
Jiang
,
S.
,
Luo
,
J.
,
Ruiz-Pava
,
G.
,
Hu
,
J.
, and
Magee
,
C. L.
,
2021
, “
Deriving Design Feature Vectors for Patent Images Using Convolutional Neural Networks
,”
ASME J. Mech. Des.
,
143
(
6
), p.
061405
.
26.
McTeague
,
C.
, and
Chatzimichali
,
A.
,
2022
, “
Exploiting Patent Knowledge in Engineering Design: A Cognitive Basis for Remodeling Patent Documents
,”
Proc. CIRP
,
109
, pp.
401
406
.
27.
Goucher-Lambert
,
K.
,
Gyory
,
J. T.
,
Kotovsky
,
K.
, and
Cagan
,
J.
,
2020
, “
Adaptive Inspirational Design Stimuli: Using Design Output to Computationally Search for Stimuli That Impact Concept Generation
,”
ASME J. Mech. Des.
,
142
(
9
), p.
091401
.
28.
Goucher-Lambert
,
K.
, and
Kwon
,
E.
,
2023
, “
Examining the Boundary Between Near and Far Design Stimuli
,”
Proc. Des. Soc.
,
3
, pp.
1725
1734
.
29.
Luo
,
J.
,
Sarica
,
S.
, and
Wood
,
K. L.
,
2021
, “
Guiding Data-Driven Design Ideation by Knowledge Distance
,”
Knowl.-Based Syst.
,
218
, p.
106873
.
30.
Kenett
,
Y. N.
, and
Faust
,
M.
,
2019
, “
A Semantic Network Cartography of the Creative Mind
,”
Trends Cogn. Sci.
,
23
(
4
), pp.
271
274
.
31.
Jiang
,
S.
,
Hu
,
J.
,
Wood
,
K. L.
, and
Luo
,
J.
,
2022
, “
Data-Driven Design-By-Analogy: State-of-the-Art and Future Directions
,”
ASME J. Mech. Des.
,
144
(
2
), p.
020801
.
32.
Chen
,
L.
,
Wang
,
P.
,
Dong
,
H.
,
Shi
,
F.
,
Han
,
J.
,
Guo
,
Y.
,
Childs
,
P. R. N.
,
Xiao
,
J.
, and
Wu
,
C.
,
2019
, “
An Artificial Intelligence Based Data-Driven Approach for Design Ideation
,”
J. Vis. Commun. Image Represent.
,
61
, pp.
10
22
.
33.
Jia
,
J.
,
Zhang
,
Y.
, and
Saad
,
M.
,
2022
, “
An Approach to Capturing and Reusing Tacit Design Knowledge Using Relational Learning for Knowledge Graphs
,”
Adv. Eng. Inform.
,
51
, p.
101505
.
34.
Aurisicchio
,
M.
, and
Bracewell
,
R.
,
2013
, “
Capturing an Integrated Design Information Space With a Diagram-Based Approach
,”
J. Eng. Des.
,
24
(
6
), pp.
397
428
.
35.
Sun
,
Y.
,
Liu
,
W.
,
Cao
,
G.
,
Peng
,
Q.
,
Gu
,
J.
, and
Fu
,
J.
,
2022
, “
Effective Design Knowledge Abstraction From Chinese Patents Based on a Meta-Model of the Patent Design Knowledge Graph
,”
Comput. Ind.
,
142
, p.
103749
.
36.
Noble
,
D.
, and
Rittel
,
H.
,
1988
, “
Issue-Based Information Systems for Design
,”
ACADIA ‘88
,
Ann Arbor, MI
,
Oct. 28–30
, pp.
275
286
.
37.
Allan
,
M.
,
Richard
,
Y.
,
Victoria
,
B.
, and
Thomas
,
M.
,
1991
, “
Questions, Options, and Criteria: Elements of Design Space Analysis
,”
Human-Comput. Interact.
,
6
(
3
), pp.
201
250
.
38.
Bracewell
,
R.
,
Wallace
,
K.
,
Moss
,
M.
, and
Knott
,
D.
,
2009
, “
Capturing Design Rationale
,”
Comput.-Aided Des.
,
41
(
3
), pp.
173
186
.
39.
Poorkiany
,
M.
,
Johansson
,
J.
, and
Elgh
,
F.
,
2016
, “
Capturing, Structuring and Accessing Design Rationale in Integrated Product Design and Manufacturing Processes
,”
Adv. Eng. Inform.
,
30
(
3
), pp.
522
536
.
40.
Liu
,
J.
, and
Hu
,
X.
,
2013
, “
A Reuse Oriented Representation Model for Capturing and Formalizing the Evolving Design Rationale
,”
Artif. Intell. Eng. Des., Anal. Manuf.
,
27
(
4
), pp.
401
413
.
41.
Chen
,
X.
,
Zhang
,
M.
,
Xiong
,
S.
, and
Qian
,
T.
,
2022
, “
On the Form of Parsed Sentences for Relation Extraction
,”
Knowl.-Based Syst.
,
251
, p.
109184
.
42.
Tang
,
R.
,
Chen
,
Y.
,
Huang
,
R.
, and
Qin
,
Y.
,
2023
, “
Enhancing Interaction Representation for Joint Entity and Relation Extraction
,”
Cogn. Syst. Res.
,
82
, p.
101153
.
43.
Tang
,
R.
,
Chen
,
Y.
,
Qin
,
Y.
,
Huang
,
R.
, and
Zheng
,
Q.
,
2023
, “
Boundary Regression Model for Joint Entity and Relation Extraction
,”
Expert Syst. Appl.
,
229
, p.
120441
.
44.
Tang
,
R.
,
Chen
,
Y.
,
Qin
,
Y.
,
Huang
,
R.
,
Dong
,
B.
, and
Zheng
,
Q.
,
2022
, “
Boundary Assembling Method for Joint Entity and Relation Extraction
,”
Knowl.-Based Syst.
,
250
, p.
109129
.
45.
Huang
,
H.
,
Shang
,
Y.-M.
,
Sun
,
X.
,
Wei
,
W.
, and
Mao
,
X.
,
2022
, “
Three Birds, One Stone: A Novel Translation Based Framework for Joint Entity and Relation Extraction
,”
Knowl.-Based Syst.
,
236
, p.
107677
.
46.
Gao
,
C.
,
Zhang
,
X.
,
Li
,
L.
,
Li
,
J.
,
Zhu
,
R.
,
Du
,
K.
, and
Ma
,
Q.
,
2023
, “
ERGM: A Multi-stage Joint Entity and Relation Extraction With Global Entity Match
,”
Knowl.-Based Syst.
,
271
, p.
110550
.
47.
Deng
,
S.
,
Zhang
,
N.
,
Chen
,
H.
,
Tan
,
C.
,
Huang
,
F.
,
Xu
,
C.
, and
Chen
,
H.
,
2022
, “
Low-Resource Extraction With Knowledge-Aware Pairwise Prototype Learning
,”
Knowl.-Based Syst.
,
235
, p.
107584
.
48.
Zhang
,
Y.
, and
Lu
,
Z.
,
2019
, “
Exploring Semi-Supervised Variational Autoencoders for Biomedical Relation Extraction
,”
Methods
,
166
, pp.
112
119
.
49.
Deepika
,
S. S.
, and
Geetha
,
T. V.
,
2021
, “
Pattern-Based Bootstrapping Framework for Biomedical Relation Extraction
,”
Eng. Appl. Artif. Intell.
,
99
, p.
104130
.
50.
Xu
,
X.
, and
Cai
,
H.
,
2021
, “
Ontology and Rule-Based Natural Language Processing Approach for Interpreting Textual Regulations on Underground Utility Infrastructure
,”
Adv. Eng. Inform.
,
48
, p.
101288
.
51.
Eyal
,
M.
,
Amrami
,
A.
,
Taub-Tabib
,
H.
, and
Goldberg
,
Y.
,
2021
, “Bootstrapping Relation Extractors Using Syntactic Search by Examples.” Available: https://aclanthology.org/2021.eacl-main.128.
52.
M.
,
L.
,
D.
,
M.
, and
E.
,
O.
,
2015
, “
Classifying the Lexico-Syntactic Patterns of Semantic Relations Between Two Nouns in Romanian Language
,”
2015 International Conference on Speech Technology and Human-Computer Dialogue (SpeD)
,
Bucharest, Romania
,
Oct. 14–17
, pp.
1
6
.
53.
Yue
,
G.
,
Liu
,
J.
,
Zhang
,
Q.
, and
Hou
,
Y.
,
2023
, “
Building a Design-Rationale-Centric Knowledge Network to Realize the Internalization of Explicit Knowledge
,”
Appl. Sci.
,
13
(
3
).
54.
World Intellectual Property Organization
,
2023
, WIPO Patent Drafting Manual, Geneva, Switzerland : World Intellectual Property Organization. Available: , Accessed May 10, 2022.
55.
Salton
,
G.
,
Wong
,
A.
, and
Yang
,
C. S.
,
1975
, “
A Vector Space Model for Automatic Indexing
,”
Commun. ACM
,
18
(
11
), pp.
613
620
.
56.
Chen
,
H.
, and
Luo
,
X.
,
2019
, “
An Automatic Literature Knowledge Graph and Reasoning Network Modeling Framework Based on Ontology and Natural Language Processing
,”
Adv. Eng. Inform.
,
42
, p.
100959
.
57.
Hao
,
J.
,
Zhao
,
L.
,
Milisavljevic-Syed
,
J.
, and
Ming
,
Z.
,
2021
, “
Integrating and Navigating Engineering Design Decision-Related Knowledge Using Decision Knowledge Graph
,”
Adv. Eng. Inform.
,
50
, p.
101366
.
58.
Sarica
,
S.
, and
Luo
,
J.
,
2021
, “
Design Knowledge Representation With Technology Semantic Network
,”
Proc. Des. Soc.
,
1
, pp.
1043
1052
.
59.
Sarica
,
S.
,
Luo
,
J.
, and
Wood
,
K. L.
,
2020
, “
TechNet: Technology Semantic Network Based on Patent Data
,”
Expert Syst. Appl.
,
142
, p.
112995
.
60.
Sarica
,
S.
,
Song
,
B.
,
Luo
,
J.
, and
Wood
,
K. L.
,
2021
, “
Idea Generation With Technology Semantic Network
,”
Artif. Intell. Eng. Des., Anal. Manuf.
,
35
(
3
), pp.
265
283
.
61.
Siddharth
,
L.
,
Blessing
,
L. T. M.
,
Wood
,
K. L.
, and
Luo
,
J.
,
2021
, “
Engineering Knowledge Graph From Patent Database
,”
ASME J. Comput. Inf. Sci. Eng.
,
22
(
2
), p.
021008
.
62.
Liu
,
H.
,
Li
,
W.
, and
Li
,
Y.
,
2021
, “
A New Computational Method for Acquiring Effect Knowledge to Support Product Innovation
,”
Knowl.-Based Syst.
,
231
, p.
107410
.
63.
Fantoni
,
G.
,
Apreda
,
R.
,
Dell Orletta
,
F.
, and
Monge
,
M.
,
2013
, “
Automatic Extraction of Function–Behaviour–State Information From Patents
,”
Adv. Eng. Inform.
,
27
(
3
), pp.
317
334
.
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