Abstract

We propose a large, scalable engineering knowledge graph, comprising sets of real-world engineering “facts” as < entity, relationship, entity > triples that are found in the patent database. We apply a set of rules based on the syntactic and lexical properties of claims in a patent document to extract facts. We aggregate these facts within each patent document and integrate the aggregated sets of facts across the patent database to obtain an engineering knowledge graph. Such a knowledge graph is expected to support inference, reasoning, and recalling in various engineering tasks. The knowledge graph has a greater size and coverage in comparison with the previously used knowledge graphs and semantic networks in the engineering literature.

References

1.
Singhal
,
A.
,
2012
, Introducing the Knowledge Graph: Things, not Strings, https://blog.google/products/search/introducing-knowledge-graph-things-not/, Accessed March 1, 2021.
2.
Paulheim
,
H.
,
2017
, “
Knowledge Graph Refinement: A Survey of Approaches and Evaluation Methods
,”
Semant. Web
,
8
(
3
), pp.
489
508
.
3.
Ehrlinger
,
L.
, and
Wöß
,
W.
,
2016
, “
Towards a Definition of Knowledge Graphs
,”
Semantics (Posters, Demos, SuCCESS)
,
48
(
1
), pp.
1
4
.
4.
Chen
,
X.
,
Jia
,
S.
, and
Xiang
,
Y.
,
2020
, “
A Review: Knowledge Reasoning Over Knowledge Graph
,”
Expert Syst. Appl.
,
141
(
1
), p.
112948
.
5.
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
.
6.
Siddharth
,
L.
, and
Sarkar
,
P.
,
2017
, “
A Methodology for Predicting the Effect of Engineering Design Changes
,”
Procedia CIRP
,
60
(
1
), pp.
452
457
.
7.
Aurisicchio
,
M.
,
Bracewell
,
R.
, and
Hooey
,
B. L.
,
2016
, “
Rationale Mapping and Functional Modelling Enhanced Root Cause Analysis
,”
Saf. Sci.
,
85
(
1
), pp.
241
257
.
8.
Siddharth
,
L.
,
Chakrabarti
,
A.
, and
Ranganath
,
R.
,
2019
, “
Modeling and Structuring Design Rationale to Enable Knowledge Reuse
,”
Syst. Eng.
,
23
(
3
), pp.
294
311
.
9.
Siddharth
,
L.
, and
Chakrabarti
,
A.
,
2018
, “
Evaluating the Impact of Idea-Inspire 4.0 on Analogical Transfer of Concepts
,”
Artif. Intell. Eng. Des. Anal. Manuf.
,
32
(
4
), pp.
431
448
.
10.
Browning
,
T. R.
,
2001
, “
Applying the Design Structure Matrix to System Decomposition and Integration Problems: A Review and New Directions
,”
IEEE Trans. Eng. Manage.
,
48
(
3
), pp.
292
306
.
11.
Siddharth
,
L.
,
Chakrabarti
,
A.
, and
Venkataraman
,
S.
,
2018
, “
Representing Complex Analogues Using a Function Model to Support Conceptual Design
,”
Volume 1B: 38th Computers and Information in Engineering Conference
,
Quebec City, Canada
,
Aug. 26–29
, p. V01BT02A039, No. 51739.
12.
Chakrabarti
,
A.
,
Siddharth
,
L.
,
Dinakar
,
M.
,
Panda
,
M.
,
Palegar
,
N.
, and
Keshwani
,
S.
,
2017
, “
Idea-Inspire 3.0 – A Tool for Analogical Design
,”
International Conference on Research into Design (ICoRD'17)
,
Guwahati, India
,
Jan. 9–11
, Vol. 2, pp.
475
485
.
13.
Siddharth
,
L.
,
Madhusudanan
,
N.
, and
Chakrabarti
,
A.
,
2019
, “
Toward Automatically Assessing the Novelty of Engineering Design Solutions
,”
ASME J. Comput. Inf. Sci. Eng.
,
20
(
1
), p.
011001
.
14.
Han
,
J.
,
Shi
,
F.
,
Chen
,
L.
, and
Childs
,
P. R. N.
,
2018
, “
The Combinator—A Computer-Based Tool for Creative Idea Generation Based on a Simulation Approach
,”
Des. Sci.
,
4
(
1
), p.
e11
.
15.
Chen
,
T.-J.
, and
Krishnamurthy
,
V. R.
,
2020
, “
Investigating a Mixed-Initiative Workflow for Digital Mind-Mapping
,”
ASME J. Mech. Des.
,
142
(
10
), p.
101404
.
16.
Camburn
,
B.
,
He
,
Y.
,
Raviselvam
,
S.
,
Luo
,
J.
, and
Wood
,
K.
,
2020
, “
Machine Learning-Based Design Concept Evaluation
,”
ASME J. Mech. Des.
,
142
(
3
), p.
031113
.
17.
Noh
,
H.
,
Jo
,
Y.
, and
Lee
,
S.
,
2015
, “
Keyword Selection and Processing Strategy for Applying Text Mining to Patent Analysis
,”
Expert Syst. Appl.
,
42
(
9
), pp.
4348
4360
.
18.
Luo
,
J.
,
Sarica
,
S.
, and
Wood
,
K. L.
,
2021
, “
Guiding Data-Driven Design Ideation by Knowledge Distance
,”
Knowl.-Based Syst.
,
218
(
1
), pp.
106873
.
19.
Soo
,
V.-W.
,
Lin
,
S.-Y.
,
Yang
,
S.-Y.
,
Lin
,
S.-N.
, and
Cheng
,
S.-L.
,
2006
, “
A Cooperative Multi-Agent Platform for Invention Based on Patent Document Analysis and Ontology
,”
Expert Syst. Appl.
,
31
(
4
), pp.
766
775
.
20.
Korobkin
,
D.
,
Fomenkov
,
S.
,
Kravets
,
A.
,
Kolesnikov
,
S.
, and
Dykov
,
M.
,
2015
, “
Three-Steps Methodology for Patents Prior-Art Retrieval and Structured Physical Knowledge Extracting
,”
Commun. Comput. Inf.
,
535
(
1
), pp.
124
136
.
21.
Chen
,
P.
,
Lu
,
Y.
,
Zheng
,
V. W.
,
Chen
,
X.
, and
Yang
,
B.
,
2018
, “
KnowEdu: A System to Construct Knowledge Graph for Education
,”
IEEE Access
,
6
(
1
), pp.
31553
31563
.
22.
Bordes
,
A.
,
Usunier
,
N.
,
Garcia-Duran
,
A.
,
Weston
,
J.
, and
Yakhnenko
,
O.
,
2013
, “Translating Embeddings for Modeling Multi-Relational Data,”
Advances in Neural Information Processing Systems 26
,
C. J. C.
Burges
,
L.
Bottou
,
M.
Welling
,
Z.
Ghahramani
, and
K. Q.
Weinberger
, eds.,
Curran Associates, Inc.
, pp.
2787
2795
. http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf
23.
Park
,
N.
,
Kan
,
A.
,
Dong
,
X. L.
,
Zhao
,
T.
, and
Faloutsos
,
C.
,
2019
, “
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
,”
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
,
New York
,
Aug. 4–8
, pp.
596
606
.
24.
Lehmann
,
J.
,
Isele
,
R.
,
Jakob
,
M.
,
Jentzsch
,
A.
,
Kontokostas
,
D.
,
Mendes
,
P. N.
,
Hellmann
,
S.
,
Morsey
,
M.
,
van Kleef
,
P.
,
Auer
,
S.
, and
Bizer
,
C.
,
2015
, “
DBpedia—A Large-Scale, Multilingual Knowledge Base Extracted From Wikipedia
,”
Semant. Web
,
6
(
2
), pp.
167
195
.
25.
Speer
,
R.
,
Chin
,
J.
, and
Havasi
,
C.
,
2017
, “
ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
,” AAAI, Vol. 31, No. 1, https://ojs.aaai.org/index.php/AAAI/article/view/11164, Accessed February 23, 2021.
26.
Jiang
,
J. J.
, and
Conrath
,
D. W.
,
1997
, “
Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy
,” arXiv preprint cmp-lg/9709008.
27.
Chiu
,
I.
, and
Shu
,
L.
,
2007
, “
Biomimetic Design Through Natural Language Analysis to Facilitate Cross-Domain Information Retrieval
,”
Artif Intell Eng Des Anal Manuf AI EDAM
,
21
(
1
), pp.
45
59
.
28.
Linsey
,
J.
,
Markman
,
A.
, and
Wood
,
K.
,
2012
, “
Design by Analogy: A Study of the WordTree Method for Problem Re-Representation
,”
ASME J. Mech. Des.
,
134
(
4
), p.
041009
.
29.
Kan
,
J. W. T.
, and
Gero
,
J. S.
,
Feb. 2018
, “
Characterizing Innovative Processes in Design Spaces Through Measuring the Information Entropy of Empirical Data From Protocol Studies
,”
Artif. Intell. Eng. Des. Anal. Manuf.
,
32
(
1
), pp.
32
43
.
30.
Georgiev
,
G. V.
, and
Georgiev
,
D. D.
,
2018
, “
Enhancing User Creativity: Semantic Measures for Idea Generation
,”
Knowl.-Based Syst.
,
151
(
1
), pp.
1
15
.
31.
He
,
Y.
,
Camburn
,
B.
,
Liu
,
H.
,
Luo
,
J.
,
Yang
,
M.
, and
Wood
,
K. L.
,
2019
, “
Mining and Representing the Concept Space of Existing Ideas for Directed Ideation
,”
ASME J. Mech. Des.
,
141
(
12
), p.
121101
.
32.
Goucher-Lambert
,
K.
, and
Cagan
,
J.
,
2019
, “
Crowdsourcing Inspiration: Using Crowd Generated Inspirational Stimuli to Support Designer Ideation
,”
Des. Stud.
,
61
(
1
), pp.
1
29
.
33.
Han
,
J.
,
Shi
,
F.
,
Chen
,
L.
, and
Childs
,
P. R. N.
,
2018
, “
A Computational Tool for Creative Idea Generation Based on Analogical Reasoning and Ontology
,”
Artif. Intell. Eng. Des. Anal. Manuf.
,
32
(
4
), pp.
462
477
.
34.
Yamamoto
,
E.
,
Taura
,
T.
,
Ohashi
,
S.
, and
Yamamoto
,
M.
,
2010
, “
A Method for Function Dividing in Conceptual Design by Focusing on Linguistic Hierarchal Relations
,”
ASME J. Comput. Inf. Sci. Eng.
,
10
(
3
), p.
031004
.
35.
Pantel
,
P.
, and
Pennacchiotti
,
M.
,
2008
, “
Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations
,”
Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics
,
Sydney, Australia
,
July 17–18
, pp.
113
120
.
36.
Park
,
S.-M.
,
Kim
,
Y.-G.
, and
Baik
,
D.-K.
,
2016
, “
Sentiment Root Cause Analysis Based on Fuzzy Formal Concept Analysis and Fuzzy Cognitive Map
,”
ASME J. Comput. Inf. Sci. Eng.
,
16
(
3
), p.
031004
.
37.
Wu
,
Z.
, and
Palmer
,
M.
,
1994
, “
Verbs Semantics
and Lexical Selection,”
Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics
,
Stroudsburg, PA
,
June 27–30
, pp.
133
138
.
38.
Li
,
X.
,
Chen
,
C.-H.
,
Zheng
,
P.
,
Wang
,
Z.
,
Jiang
,
Z.
, and
Jiang
,
Z.
,
2020
, “
A Knowledge Graph-Aided Concept-Knowledge Approach for Evolutionary Smart Product-Service System Development
,”
ASME J. Mech. Des.
,
142
(
10
), p.
101403
.
39.
Hatchuel
,
A.
,
Le Masson
,
P.
, and
Weil
,
B.
,
2011
, “
Teaching Innovative Design Reasoning: How Concept-Knowledge Theory Can Help Overcome Fixation Effects
,”
Artif. Intell. Eng. Des. Anal. Manuf.
,
25
(
1
), pp.
77
92
.
40.
Cheong
,
H.
,
Li
,
W.
,
Cheung
,
A.
,
Nogueira
,
A.
, and
Iorio
,
F.
,
2017
, “
Automated Extraction of Function Knowledge From Text
,”
ASME J. Mech. Des.
,
139
(
11
), p.
111407
.
41.
Mikolov
,
T.
,
Chen
,
K.
,
Corrado
,
G.
, and
Dean
,
J.
,
2013
, “Efficient Estimation of Word Representations in Vector Space,” arXiv preprint arXiv:1301.3781.
42.
Cascini
,
G.
, and
Russo
,
D.
,
2006
, “
Computer-Aided Analysis of Patents and Search for TRIZ Contradictions
,”
Int. J. Prod. Dev.
,
4
(
1–2
), pp.
52
67
.
43.
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
.
44.
Song
,
B.
,
Luo
,
J.
, and
Wood
,
K.
,
2019
, “
Data-Driven Platform Design: Patent Data and Function Network Analysis
,”
ASME J. Mech. Des.
,
141
(
2)
), p.
021101
.
45.
Song
,
B.
, and
Luo
,
J.
,
2017
, “
Mining Patent Precedents for Data-Driven Design: The Case of Spherical Rolling Robots
,”
ASME J. Mech. Des.
,
139
(
11
), p.
111420
.
46.
Song
,
H.
, and
Fu
,
K.
,
2019
, “
Design-by-Analogy: Exploring for Analogical Inspiration With Behavior, Material, and Component-Based Structural Representation of Patent Databases
,”
ASME J. Comput. Inf. Sci. Eng.
,
19
(
2)
), p.
021014
.
47.
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
.
48.
Hirtz
,
J.
,
Stone
,
R.
,
McAdams
,
D.
,
Szykman
,
S.
, and
Wood
,
K.
,
2002
, “
A Functional Basis for Engineering Design: Reconciling and Evolving Previous Efforts
,”
Res. Eng. Des.
,
13
(
2
), pp.
65
82
.
49.
Tseng
,
Y.-H.
,
Lin
,
C.-J.
, and
Lin
,
Y.-I.
,
2007
, “
Text Mining Techniques for Patent Analysis
,”
Inf. Process. Manag.
,
43
(
5
), pp.
1216
1247
.
50.
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
.
51.
Bonaccorsi
,
A.
, and
Fantoni
,
G.
,
2007
, “
Expanding the Functional Ontology in Conceptual Design
,”
DS 42: Proceedings of ICED 2007, the 16th International Conference on Engineering Design
, Vol.
28
,
Paris, France
,
July 7
, pp.
723
724
.
52.
Mao
,
X.
, and
Sen
,
C.
,
2020
, “
Semantic and Qualitative Physics-Based Reasoning on Plain-English Flow Terms for Generating Function Model Alternatives
,”
ASME J. Comput. Inf. Sci. Eng.
,
20
(
4
), p.
041006
.
53.
Mao
,
X.
, and
Sen
,
C.
,
2018
, “
Physics-Based Semantic Reasoning
for Function Model Decomposition,”
Volume 1A: 38th Computers and Information in Engineering Conference
,
Quebec City Convention Center, Quebec City, Canada
,
Aug. 26
.
54.
Sarica
,
S.
,
Luo
,
J.
, and
Wood
,
K. L.
,
2020
, “
TechNet: Technology Semantic Network Based on Patent Data
,”
Expert Syst. Appl.
,
142
(
1
), p.
112995
.
55.
Mayfield
,
J.
, and
Finin
,
T.
,
2012
, “
Evaluating the Quality of a Knowledge Base Populated From Text
,”
Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-Scale Knowledge Extraction
,
Montréal, Canada
,
June
, pp.
68
73
.
56.
Havasi
,
C.
,
Speer
,
R.
, and
Alonso
,
J.
,
2007
, “
ConceptNet: A Lexical Resource for Common Sense Knowledge
,”
Int. Conf. Recent Adv. Nat. Lang. Process. RANLP
,
309
(
1
), pp.
269
280
.
57.
Mukherjee
,
S.
, and
Joshi
,
S.
,
2013
, “
Sentiment Aggregation Using ConceptNet Ontology
,”
Proceedings of the Sixth International Joint Conference on Natural Language Processing
,
Nagoya, Japan
,
October
, pp.
570
578
.
58.
Agarwal
,
B.
,
Poria
,
S.
,
Mittal
,
N.
,
Gelbukh
,
A.
, and
Hussain
,
A.
,
2015
, “
Concept-Level Sentiment Analysis With Dependency-Based Semantic Parsing: A Novel Approach
,”
Cogn. Comput.
,
7
(
4
), pp.
487
499
.
59.
Jamrozik
,
A.
, and
Gentner
,
D.
,
2020
, “
Relational Labeling Unlocks Inert Knowledge
,”
Cognition
,
196
(
1
), p.
104146
.
60.
Sarica
,
S.
,
Song
,
B.
,
Luo
,
J.
, and
Wood
,
K. L.
,
2021
, “
Idea Generation With Technology Semantic Network
,”
Artif. Intell. Eng. Des. Anal. Manuf.
, FirstView, pp.
1
19
.
61.
Pennington
,
J.
,
Socher
,
R.
, and
Manning
,
C.
,
2014
, “
Glove: Global Vectors for Word Representation
,”
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
,
Doha, Qatar
,
October
, pp.
1532
1543
.
62.
Shi
,
F.
,
Chen
,
L.
,
Han
,
J.
, and
Childs
,
P.
,
2017
, “
A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval
,”
ASME J. Mech. Des.
,
139
(
11
), p.
111402
.
63.
Lu
,
R.
,
Jin
,
X.
,
Zhang
,
S.
,
Qiu
,
M.
, and
Wu
,
X.
,
2019
, “
A Study on Big Knowledge and Its Engineering Issues
,”
IEEE Trans. Knowl. Data Eng.
,
31
(
9
), pp.
1630
1644
.
64.
Altshuller
,
G.
,
Shulyak
,
L.
, and
Rodman
,
S.
,
1999
,
The Innovation Algorithm: TRIZ, Systematic Innovation and Technical Creativity
,
Technical Innovation Center, Inc.
,
Worcester, MA
.
65.
Vincent
,
J. F.
,
Bogatyreva
,
O.
,
Pahl
,
A.-K.
,
Bogatyrev
,
N.
, and
Bowyer
,
A.
,
2005
, “
Putting Biology Into TRIZ: A Database of Biological Effects
,”
Creat. Innov. Manag.
,
14
(
1
), pp.
66
72
.
66.
Cascini
,
G.
, and
Rissone
,
P.
,
2004
, “
Plastics Design: Integrating TRIZ Creativity and Semantic Knowledge Portals
,”
J. Eng. Des.
,
15
(
4
), pp.
405
424
.
You do not currently have access to this content.