With the advent of the big-data era, massive information stored in electronic and digital forms on the internet become valuable resources for knowledge discovery in engineering design. Traditional document retrieval method based on document indexing focuses on retrieving individual documents related to the query, but is incapable of discovering the various associations between individual knowledge concepts. Ontology-based technologies, which can extract the inherent relationships between concepts by using advanced text mining tools, can be applied to improve design information retrieval in the large-scale unstructured textual data environment. However, few of the public available ontology database stands on a design and engineering perspective to establish the relations between knowledge concepts. This paper develops a “WordNet” focusing on design and engineering associations by integrating the text mining approaches to construct an unsupervised learning ontology network. Subsequent probability and velocity network analysis are applied with different statistical behaviors to evaluate the correlation degree between concepts for design information retrieval. The validation results show that the probability and velocity analysis on our constructed ontology network can help recognize the high related complex design and engineering associations between elements. Finally, an engineering design case study demonstrates the use of our constructed semantic network in real-world project for design relations retrieval.

References

References
1.
Bertola
,
P.
, and
Teixeira
,
J. C.
,
2003
, “
Design as a Knowledge Agent: How Design as a Knowledge Process is Embedded Into Organizations to Foster Innovation
,”
Des. Stud.
,
24
(
2
), pp.
181
194
.
2.
Ullman
,
D.
,
2015
,
The Mechanical Design Process
,
McGraw-Hill Higher Education
, New York.
3.
Chandrasegaran
,
S. K.
,
Ramani
,
K.
,
Sriram
,
R. D.
,
Horváth
,
I.
,
Bernard
,
A.
,
Harik
,
R. F.
, and
Gao
,
W.
,
2013
, “
The Evolution, Challenges, and Future of Knowledge Representation in Product Design Systems
,”
Comput.-Aided Des.
,
45
(
2
), pp.
204
228
.
4.
Tuarob
,
S.
, and
Tucker
,
C. S.
,
2015
, “
Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social Media Networks
,”
ASME J. Mech. Des.
,
137
(
7
), p.
071402
.
5.
Ma
,
J.
, and
Kim
,
H. M.
,
2014
, “
Continuous Preference Trend Mining for Optimal Product Design With Multiple Profit Cycles
,”
ASME J. Mech. Des.
,
136
(
6
), p.
061002
.
6.
Ishino
,
Y.
, and
Jin
,
Y.
,
2001
, “
Data Mining for Knowledge Acquisition in Engineering Design
,”
Data Mining for Design and Manufacturing
,
Springer
, Boston, MA, pp.
145
160
.
7.
Li
,
Z.
, and
Ramani
,
K.
,
2007
, “
Ontology-Based Design Information Extraction and Retrieval
,”
Artif. Intell. Eng. Des., Anal. Manuf.
,
21
(
2
), pp.
137
154
.
8.
Sangelkar
,
S.
, and
McAdams
,
D. A.
,
2012
, “
Adapting ADA Architectural Design Knowledge for Universal Product Design Using Association Rule Mining: A Function Based Approach
,”
ASME J. Mech. Des.
,
134
(
7
), p.
071003
.
9.
Lan
,
L.
,
Liu
,
Y.
,
Lu
,
W. F.
, and
Alghamdi
,
A.
,
2015
, “
Automatic Discovery of Design Task Structure Using Deep Belief Nets
,”
ASME
Paper No. DETC2015-47369.
10.
Ur-Rahman
,
N.
, and
Harding
,
J. A.
,
2012
, “
Textual Data Mining for Industrial Knowledge Management and Text Classification: A Business Oriented Approach
,”
Expert Syst. Appl.
,
39
(
5
), pp.
4729
4739
.
11.
McMahon
,
C.
,
Lowe
,
A.
,
Culley
,
S.
,
Corderoy
,
M.
,
Crossland
,
R.
,
Shah
,
T.
, and
Stewart
,
D.
,
2004
, “
Waypoint: An Integrated Search and Retrieval System for Engineering Documents
,”
ASME J. Comput. Inf. Sci. Eng.
,
4
(
4
), pp.
329
338
.
12.
Homer
,
G. R.
,
Thompson
,
D. M.
, and
Deacon
,
M.
,
2002
, “
A Distributed Document Management System
,”
Comput. Control Eng. J.
,
13
(
6
), pp.
315
318
.
13.
Chen
,
Y.-M.
, and
Jan
,
Y.-D.
,
2000
, “
Enabling Allied Concurrent Engineering Through Distributed Engineering Information Management
,”
Rob. Comput. Integr. Manuf.
,
16
(
1
), pp.
9
27
.
14.
Salton
,
G.
, and
McGill
,
M. J.
,
1986
,
Introduction to Modern Information Retrieval
, Facet Publishing, London.
15.
Salton
,
G.
, and
Buckley
,
C.
,
1988
, “
Term-Weighting Approaches in Automatic Text Retrieval
,”
Inf. Process. Manage.
,
24
(
5
), pp.
513
523
.
16.
Murphy
,
J.
,
Fu
,
K.
,
Otto
,
K.
,
Yang
,
M.
,
Jensen
,
D.
, and
Wood
,
K.
,
2014
, “
Function Based Design-by-Analogy: A Functional Vector Approach to Analogical Search
,”
ASME J. Mech. Des.
,
136
(
10
), p.
101102
.
17.
Yu
,
W.
, and
Hsu
,
J.-Y.
,
2013
, “
Content-Based Text Mining Technique for Retrieval of CAD Documents
,”
Autom. Constr.
,
31
, pp.
65
74
.
18.
Iyer
,
N.
,
Jayanti
,
S.
,
Lou
,
K.
,
Kalyanaraman
,
Y.
, and
Ramani
,
K.
,
2005
, “
Shape-Based Searching for Product Lifecycle Applications
,”
Comput Aided Des.
,
37
(
13
), pp.
1435
1446
.
19.
Brin
,
S.
, and
Page
,
L.
,
2012
, “
Reprint of: The Anatomy of a Large-Scale Hypertextual Web Search Engine
,”
Comput. Networks
,
56
(
18
), pp.
3825
3833
.
20.
Glier
,
M. W.
,
McAdams
,
D. A.
, and
Linsey
,
J. S.
,
2014
, “
Exploring Automated Text Classification to Improve Keyword Corpus Search Results for Bioinspired Design
,”
ASME J. Mech. Des.
,
136
(
11
), p.
111103
.
21.
Lim
,
S.
, and
Tucker
,
C. S.
,
2016
, “
A Bayesian Sampling Method for Product Feature Extraction From Large-Scale Textual Data
,”
ASME J. Mech. Des.
,
138
(
6
), p.
061403
.
22.
Lan
,
L.
,
Liu
,
Y.
, and
Lu
,
W. F.
,
2016
, “
Discovering a Hierarchical Design Process Model Using Text Mining
,”
ASME
Paper No. DETC2016-59829.
23.
Rezgui
,
Y.
,
Boddy
,
S.
,
Wetherill
,
M.
, and
Cooper
,
G.
,
2011
, “
Past, Present and Future of Information and Knowledge Sharing in the Construction Industry: Towards Semantic Service-Based e-Construction?
,”
Comput.-Aided Des.
,
43
(
5
), pp.
502
515
.
24.
Chang
,
X.
,
Rai
,
R.
, and
Terpenny
,
J.
,
2010
, “
Development and Utilization of Ontologies in Design for Manufacturing
,”
ASME J. Mech. Des.
,
132
(
2
), p.
021009
.
25.
Liu
,
Y.
,
Lim
,
S. C. J.
, and
Lee
,
W. B.
,
2013
, “
Product Family Design Through Ontology-Based Faceted Component Analysis, Selection, and Optimization
,”
ASME J. Mech. Des.
,
135
(
8
), p.
081007
.
26.
Dong
,
A.
, and
Agogino
,
A. M.
,
1997
, “
Text Analysis for Constructing Design Representations
,”
Artif. Intell. Eng.
,
11
(
2
), pp.
65
75
.
27.
Princeton University
,
2010
, “
About WordNet
,” Princeton University, Princeton, NJ, accessed Apr. 21, 2017, http://wordnet.princeton.edu
28.
Speer
,
R.
, and
Havasi
,
C.
,
2012
, “
Representing General Relational Knowledge in ConceptNet 5
,”
International Conference on Language Resources and Evaluation
(
LREC
), Istanbul, Turkey, May 21–27, pp.
3679
3686
.
29.
Luminoso
,
2017
, “
ConceptNet
,” Luminoso, Cambridge, MA, accessed Apr. 21, 2017, http://conceptnet.io/
30.
Carlson
,
A.
,
Betteridge
,
J.
,
Kisiel
,
B.
,
Settles
,
B.
,
Hruschka
,
E. R.
, Jr.
, and
Mitchell
,
T. M.
,
2010
, “
Toward an Architecture for Never-Ending Language Learning
,”
24th AAAI Conference on Artificial Intelligence
, Atlanta, GA, July 11–15, pp. 1306–1313.
31.
Suchanek
,
F. M.
,
Kasneci
,
G.
, and
Weikum
,
G.
,
2007
, “
Yago: A Core of Semantic Knowledge Unifying WordNet and Wikipedia
,”
16th International Conference on World Wide Web
(
WWW
), Banff, AB, Canada, May 8–12, pp.
697
706
.
32.
Ahmed
,
S.
,
Kim
,
S.
, and
Wallace
,
K. M.
,
2007
, “
A Methodology for Creating Ontologies for Engineering Design
,”
ASME J. Comput. Inf. Sci. Eng.
,
7
(
2
), pp.
132
140
.
33.
Ohsawa
,
Y.
,
Benson
,
N. E.
, and
Yachida
,
M.
,
1998
, “
Keygraph: Automatic Indexing by Co-Occurrence Graph Based on Building Construction Metaphor
,” IEEE International Forum on Research and Technology Advances in Digital Libraries (
ADL
), Santa Barbara, CA, Apr. 22–24, pp.
12
18
.
34.
Munoz
,
D.
, and
Tucker
,
C. S.
,
2016
, “
Modeling the Semantic Structure of Textually Derived Learning Content and Its Impact on Recipients' Response States
,”
ASME J. Mech. Des.
,
138
(
4
), p.
042001
.
35.
Bullinaria
,
J. A.
, and
Levy
,
J. P.
,
2007
, “
Extracting Semantic Representations From Word Co-Occurrence Statistics: A Computational Study
,”
Behav. Res Methods
,
39
(
3
), pp.
510
526
.
36.
Tous
,
R.
, and
Delgado
,
J.
,
2006
, “
A Vector Space Model for Semantic Similarity Calculation and OWL Ontology Alignment
,”
International Conference on Database and Expert Systems Applications
(
DEXA
), Kraków, Poland, Sept. 4–8, pp.
307
316
.
37.
Juršič
,
M.
,
Sluban
,
B.
,
Cestnik
,
B.
,
Grčar
,
M.
, and
Lavrač
,
N.
,
2012
, “
Bridging Concept Identification for Constructing Information Networks From Text Documents
,”
Bisociative Knowledge Discovery
,
Springer
, Berlin, pp.
66
90
.
38.
Lim
,
S. C. J.
,
Liu
,
Y.
, and
Lee
,
W. B.
,
2011
, “
A Methodology for Building a Semantically Annotated Multi-Faceted Ontology for Product Family Modelling
,”
Adv. Eng. Inf.
,
25
(
2
), pp.
147
161
.
39.
Li
,
Z.
,
Liu
,
M.
,
Anderson
,
D. C.
, and
Ramani
,
K.
,
2005
, “
Semantics-Based Design Knowledge Annotation and Retrieval
,”
ASME
Paper No. DETC2005-85107.
40.
GuoDong
,
Z.
,
Jian
,
S.
,
Jie
,
Z.
, and
Min
,
Z.
,
2005
, “
Exploring Various Knowledge in Relation Extraction
,”
43rd Annual Meeting on Association for Computational Linguistics
, Ann Arbor, MI, June 25–30, pp.
427
434
.
41.
Sun
,
A.
, and
Grishman
,
R.
,
2012
, “
Active Learning for Relation Type Extension With Local and Global Data Views
,”
21st ACM International Conference on Information and Knowledge Management
(
CIKM
), Maui, HI, Oct. 29–Nov. 2, pp.
1105
1112
.
42.
Socher
,
R.
,
Huval
,
B.
,
Manning
,
C. D.
, and
Ng
,
A. Y.
,
2012
, “
Semantic Compositionality Through Recursive Matrix-Vector Spaces
,”
Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
(
EMNLP-CoNLL
), Jeju Island, South Korea, July 12–14, pp.
1201
1211
.
43.
Grüninger
,
M.
, and
Fox
,
M. S.
,
1995
, “
Methodology for the Design and Evaluation of Ontologies
,”
IJCAI Workshop on Basic Ontological Issues in Knowledge Sharing
, Montreal, QC, Canada, July, pp. 1–10.
44.
Uschold
,
M.
, and
King
,
M.
,
1995
, “
Towards a Methodology for Building Ontologies
,” IJCAI Workshop on Basic Ontological Issues in Knowledge Sharing, Montreal, QC, Canada, July, Paper No.
AIAI-TR-183
.
45.
Fernández-López
,
M.
,
Gómez-Pérez
,
A.
, and
Juristo
,
N.
,
1997
, “
Methontology: From Ontological Art Towards Ontological Engineering
,”
Symposium on Ontological Engineering of AAAI
, Stanford, CA, Mar. 24–26, pp. 33–40.
46.
Lim
,
S. C. J.
,
Liu
,
Y.
, and
Lee
,
W. B.
,
2010
, “
Multi-Facet Product Information Search and Retrieval Using Semantically Annotated Product Family Ontology
,”
Inf. Process. Manage.
,
46
(
4
), pp.
479
493
.
47.
Bateman
,
J. A.
,
Hois
,
J.
,
Ross
,
R.
, and
Tenbrink
,
T.
,
2010
, “
A Linguistic Ontology of Space for Natural Language Processing
,”
Artif. Intell.
,
174
(
14
), pp.
1027
1071
.
48.
Marrero
,
M.
,
Urbano
,
J.
,
Sánchez-Cuadrado
,
S.
,
Morato
,
J.
, and
Gómez-Berbís
,
J. M.
,
2013
, “
Named Entity Recognition: Fallacies, Challenges and Opportunities
,”
Comput. Stand. Interfaces
,
35
(
5
), pp.
482
489
.
49.
Sintek
,
M.
, and
Decker
,
S.
,
2002
, “
TRIPLE-A Query, Inference, and Transformation Language for the Semantic Web
,”
International Semantic Web Conference
(
ISWC
), Sardinia, Italia, June 9–12, pp.
364
378
.
50.
Rink
,
B.
,
Harabagiu
,
S.
, and
Roberts
,
K.
,
2011
, “
Automatic Extraction of Relations Between Medical Concepts in Clinical Texts
,”
J. Am. Med. Inf. Assoc.
,
18
(
5
), pp.
594
600
.
51.
Witherell
,
P.
,
Krishnamurty
,
S.
, and
Grosse
,
I. R.
,
2007
, “
Ontologies for Supporting Engineering Design Optimization
,”
ASME J. Comput. Inf. Sci. Eng.
,
7
(
2
), pp.
141
150
.
52.
Holsapple
,
C. W.
, and
Joshi
,
K. D.
,
2004
, “
A Formal Knowledge Management Ontology: Conduct, Activities, Resources, and Influences
,”
J. Assoc. Inf. Sci. Technol.
,
55
(
7
), pp.
593
612
.
53.
O'Connor
,
M.
, and
Das
,
A.
,
2009
, “
SQWRL: A Query Language for OWL
,”
Sixth International Conference on OWL: Experiences and Directions
(
OWLED
), Chantilly, VA, Oct. 23–24, pp.
208
215
.
54.
Jean
,
S.
,
Aït-Ameur
,
Y.
, and
Pierra
,
G.
,
2006
, “
Querying Ontology Based Database Using Ontoql (an Ontology Query Language)
,”
On the Move to Meaningful Internet Systems
(OTM Confederated International Conferences),
Springer
, Cham, Switzerland, pp.
704
721
.
55.
Mena
,
E.
,
Illarramendi
,
A.
,
Kashyap
,
V.
, and
Sheth
,
A. P.
,
2000
, “
Observer: An Approach for Query Processing in Global Information Systems Based on Interoperation Across Pre-Existing Ontologies
,”
Distrib. Parallel Databases
,
8
(
2
), pp.
223
271
.
56.
Scrapinghub
,
2016
, “
Scrapy—A Fast and Powerful Scraping and Web Crawling Framework
,” Scrapinghub, Cork, Ireland, accessed Nov. 23, 2016, https://scrapy.org/
57.
YankoDesign
,
2016
, “
Yan Design—Modern Industrial Design News
,” YankoDesign, accessed Nov. 23, 2016, http://www.yankodesign.com/
58.
Bird
,
S.
,
Klein
,
E.
, and
Loper
,
E.
,
2009
,
Natural Language Processing With Python
,
O'Reilly Media, Inc.
, Sebastopol, CA.
59.
Agrawal
,
R.
,
Imieliński
,
T.
, and
Swami
,
A.
,
1993
, “
Mining Association Rules Between Sets of Items in Large Databases
,”
ACM Sigmod Record
,
22
(
2
), pp.
207
216
.
60.
Serrano
,
M. Á.
,
Boguná
,
M.
, and
Vespignani
,
A.
,
2009
, “
Extracting the Multiscale Backbone of Complex Weighted Networks
,”
Proc. Natl. Acad. Sci.
,
106
(
16
), pp.
6483
6488
.
61.
Antoniou
,
I.
, and
Tsompa
,
E.
,
2008
, “
Statistical Analysis of Weighted Networks
,”
Discrete Dyn. Nat. Soc.
,
2008
, p. 375452.
62.
Dorst
,
K.
, and
Cross
,
N.
,
2001
, “
Creativity in the Design Process: Co-Evolution of Problem–Solution
,”
Des. Stud.
,
22
(
5
), pp.
425
437
.
63.
Dijkstra
,
E. W.
,
1959
, “
A Note on Two Problems in Connexion With Graphs
,”
Numer. Math.
,
1
(
1
), pp.
269
271
.
64.
Shi
,
F.
, and
Chen
,
L.
,
2016
, “
B-Link
,” Imperial College London, London, accessed Nov. 23, 2016, http://www.imperial.ac.uk/design-engineering/research/engineering-design/creativity/b-link/
65.
Childs
,
P. R.
,
2014
, “
Chapter 1—Design
,”
Mechanical Design Engineering Handbook
,
P. R.
Childs
, ed.,
Butterworth-Heinemann
,
Oxford, UK
, pp.
1
24
.
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