Sources of inspiration help designers to define the context of their designs and reflect on the emotional impact of their new products. By observing and interpreting sources of inspiration, designers form vocabularies of terms, pallets of colors, or mood boards with images, which express their feelings, inspire their creativity and help them communicate design concepts. These ideas are the motivation behind the EU-funded project TRENDS, which aimed at developing a software tool that supports the inspirational stage of design by providing designers of concept cars with various sources of inspiration. This paper concentrates on OntoTag, the semantic-based image retrieval algorithm developed within the TRENDS project, and its evaluation. OntoTag uses concepts from a general-purpose lexical ontology called OntoRo, and semantic adjectives from a domain-specific ontology for designers called CTA, to index the images in the TRENDS database in a way which provides designers with a degree of serendipity and stimulates their creativity. The semantic-based algorithm involves the following four steps: (i) creating a collection of documents and images retrieved from the web, (ii) for each document, identifying the most frequently used keywords and phrases in the text around the image, (iii) identifying the most powerful concepts represented in each document, and (iv) ranking the concepts identified and linking them to the images in the collection. OntoTag differs significantly from earlier approaches as it does not rely on machine learning and the availability of tagged corpuses. Its main innovation is in the use of the words’ monosemy and polysemy as a measure of their probability to belong to a certain concept. The proposed approach is illustrated with examples based on the software tool developed for the needs of two of the industrial collaborators involved in the TRENDS project.

1.
Eckert
,
C. M.
,
Stacey
,
M. K.
, and
Clarkson
,
P. J.
, 2000, “
Algorithms and Inspirations: Creative Reuse of Design Experience
,”
Greenwich 2000 International Symposium: Digital Creativity
, University of Greenwich, London, pp.
1
10
.
2.
Westerman
,
S. J.
,
Kaur
,
S.
,
Mougenot
,
C.
,
Sourbe
,
L.
, and
Bouchard
,
C.
, 2007, “
The Impact of Computer-Based Support on Product Designers’ Search for Inspirational Materials
,”
Proceedings of the Third I⋆PROMS International Conference
, Cardiff, UK, Jul. 2–13, pp.
581
586
.
3.
Ansburg
,
P. I.
, and
Hill
,
K.
, 2003, “
Creative and Analytic Thinkers Differ in Their Use of Attentional Resources
,” PAID, Vol.
34
.
4.
Ford
,
N.
, 1999, “
Information Retrieval and Creativity: Towards Support for the Original Thinker
,”
Journal of Documentation
,
55
(
5
), pp.
528
542
.
5.
Eckert
,
C.
, and
Stacey
,
M. K.
, 2000, “
Sources of Inspiration: A Language of Design
,”
Des. Stud.
0142-694X,
21
, pp.
99
112
.
6.
Bouchard
,
C.
, 1997, “
Modelization of the Car Design Process
,” Ph.D. thesis, ENSAM, Paris, France.
7.
Mougenot
,
C.
,
Bouchard
,
C.
, and
Aoussat
,
A.
, 2006, “
Fostering Innovation in Early Design Stage: A Study of Inspirational Process in Car-Design Companies
,”
Wonderground—Design Research Society International Conference
, Lisbon, Nov. 1–4.
8.
Lucero
,
A.
,
Aliakseyeu
,
D.
, and
Martens
,
J. -B.
, 2007, “
Augmenting Mood Boards: Flexible and Intuitive Interaction in the Context of the Design Studio
,”
Second Annual IEEE International Workshop on Horizontal Interactive Human-Computer Systems
, pp.
147
154
.
9.
McDonagh
,
D.
, and
Denton
,
H.
, 2005, “
Exploring the Degree to Which Individual Students Share a Common Perception of Specific Trend Boards: Observations Relating to Teaching, Learning and Team-Based Design
,”
Des. Stud.
0142-694X,
26
, pp.
35
53
.
10.
Gick
,
M. L.
, and
Holyoak
,
K. J.
, 1980, “
Analogical Problem Solving
,”
Cogn. Psychol.
0010-0285,
12
, pp.
306
355
.
11.
Gomes
,
P.
,
Seco
,
N.
,
Pereira
,
F. C.
,
Paiva
,
P.
,
Carreiro
,
P.
,
Ferreira
,
J. L.
, and
Bento
,
C.
, 2006, “
The Importance of Retrieval in Creating Design Analogies
,”
Knowledge-Based Systems
,
19
, pp.
480
488
.
12.
Lai
,
I. -C.
, and
Chang
,
T. -W.
, 2006, “
A Distributed Linking System for Supporting Idea Association During the Conceptual Design Stage
,”
Des. Stud.
0142-694X,
27
, pp.
685
710
.
13.
Bouchard
,
C.
,
Aoussat
,
A.
, and
Duchamp
,
R.
, 2006, “
Role of Sketching in Conceptual Design of Car Styling
,”
Journal of Design Research
,
5
(
1
), pp.
116
148
.
14.
Bonnardel
,
N.
, 2000, “
Towards Understanding and Supporting Creativity in Design: Analogies in a Constrained Cognitive Environment
,”
Knowledge-Based Systems
,
13
, pp.
505
513
.
15.
Mello
,
P.
,
Storari
,
S.
, and
Valli
,
B.
, 2008, “
A Knowledge-Based System for Fashion Trend Forecasting
,”
Lecture Notes in Artificial Intelligence
,
5027
, pp.
425
434
.
16.
Westerman
,
S. J.
, and
Kaur
,
S.
, 2007, “
Creative Industrial Design and Computer-Based Image Retrieval
,”
The Role of Aesthetics and Affect, Lecture Notes in Computer Science
,
4738
, pp.
618
629
.
17.
Vijaykumar
,
G.
,
Chakrabarti
,
A.
, 2008, “
Understanding the Knowledge Needs of Designers During Design Process in Industry
,”
ASME J. Comput. Inf. Sci. Eng.
1530-9827,
8
, p.
011004
.
18.
Cox
,
I. I.
,
Miller
,
M. L.
,
Minka
,
T. P.
,
Papathomas
,
T. V.
, and
Yianilos
,
P. N.
, 2000, “
The Bayesian Image Retrieval System, PicHunter: Theory, Implementation, and Psychophysical Experiments
,”
IEE Trans. Image Process.
,
9
(
1
), pp.
20
37
.
19.
Kuroda
,
K.
, and
Hagiwara
,
M.
, 2002, “
An Image Retrieval System by Impression Words and Specific Objects Names—IRIS
,”
Neurocomputing
0925-2312,
43
, pp.
259
276
.
20.
Gusikhin
,
O.
,
Rychtyckyj
,
N.
, and
Filev
,
D.
, 2007, “
Intelligent Systems in the Automotive Industry: Applications and Trends
,”
Knowledge Inf. Syst.
0219-1377,
12
(
2
), pp.
147
168
.
21.
Tsai
,
C. -F.
, 2007, “
A Review of Image Retrieval Methods for Digital Cultural Heritage Resources
,”
Online Information Review
,
31
(
2
), pp.
185
198
.
22.
Datta
,
R.
,
Joshi
,
D.
,
Li
,
J.
, and
Wang
,
J. Z.
, 2008, “
Image Retrieval: Ideas, Influences, and Trends of the New Age
,”
ACM Comput. Surv.
0360-0300,
40
(
2
), pp.
1
60
.
23.
Smeulders
,
A. W. M.
,
Worring
,
M.
,
Santini
,
S.
,
Gupta
,
A.
, and
Jain
,
R.
, 2000, “
Content-Based Image Retrieval at the End of the Early Years
,”
IEEE Trans. Pattern Anal. Mach. Intell.
0162-8828,
22
(
12
), pp.
1349
1380
.
24.
Liu
,
Y.
,
Zhang
,
D.
,
Lu
,
G.
, and
Ma
,
W. -Y.
, 2007, “
A Survey of Content-Based Image Retrieval With High-Level Semantics
,”
Pattern Recogn.
0031-3203,
40
, pp.
262
282
.
25.
Ferecatu
,
M.
,
Boujemaa
,
N.
, and
Crucianu
,
M.
, 2008, “
Semantic Interactive Image Retrieval Combining Visual and Conceptual Content Description
,”
J. ACM
1535-9921,
13
(
5–6
), pp.
309
322
.
26.
Enser
,
P. G. B.
,
Sandom
,
C. J.
,
Hare
,
J. S.
, and
Lewis
,
P. H.
, 2007, “
Facing the Reality of Semantic Image Retrieval
,”
Journal of Documentation
,
63
(
4
), pp.
465
481
.
27.
Hyvonen
,
E.
,
Styrman
,
A.
, and
Saarela
,
S.
, 2002, “
Ontology-Based Image Retrieval
,”
Towards the Semantic Web and Web Services. Proceedings of the XML Finland 2002 Conference
,
E.
Hyvonen
and
M.
Klemettinen
, eds.,
HIIT
,
Helsinki, Finland
, pp.
15
27
.
28.
Kiryakov
,
A.
,
Popov
,
B.
,
Terziev
,
I.
,
Manov
,
D.
, and
Ognyanoff
,
D.
, 2004, “
Semantic Annotation, Indexing, and Retrieval
,”
Web Semantics: Science, Services and Agents on the World Wide Web
,
2
(
1
), pp.
49
79
.
29.
Dill
,
S.
,
Eiron
,
N.
,
Gibson
,
D.
,
Gruhl
,
D.
,
Guha
,
R.
,
Jhingran
,
A.
,
Kanungo
,
K.
,
McCurley
,
S.
,
Rajagopalan
,
S.
,
Tomkins
,
A.
,
Tomlin
,
J. A.
, and
Zien
,
J. Y.
, 2003, “
A Case for Automated Large-Scale Semantic Annotation
,”
Web Semantics: Science, Services and Agents on the World Wide Web
,
1
(
1
), pp.
115
132
.
30.
Corcho
,
O.
, 2006, “
Ontology Based Document Annotation, Trends and Open Research Problems, International Journal of Metadata
,”
Semantics and Ontologies
,
1
(
1
), pp.
47
57
.
31.
Benjamins
,
V. R.
,
Contreras
,
J.
,
Blazquez
,
M.
,
Dodero
,
J. M.
,
Garcia
,
A.
,
Navas
,
E.
,
Hernandez
,
F.
, and
Wer
,
C.
, 2004, “
Cultural Heritage and the Semantic Web
,”
Lecture Notes in Computer Science
,
3053
, pp.
433
444
.
32.
Köhler
,
J.
,
Philippi
,
S.
,
Specht
,
M.
, and
Rüegg
,
A.
, 2006, “
Ontology Based Text Indexing and Querying for the Semantic Web
,”
Knowledge-Based Systems
,
19
(
8
), pp.
744
754
.
33.
Setchi
,
R.
, and
Tang
,
Q.
, 2007, “
Concept Indexing Using Ontology and Supervised Machine Learning, Transactions on Engineering
,”
Transactions on Engineering, Computing and Technology
,
19
, pp.
221
226
.
34.
Setchi
,
R.
, and
Tang
,
Q.
, 2007, “
Semantic-Based Representation of Content Using Concept Indexing
,”
Proceedings of the Third I⋆PROMS International Conference
, Cardiff, UK, Jul. 2–13, pp.
611
618
.
35.
Song
,
M.
,
Song
,
I. -Y.
,
Hu
,
X.
, and
Allen
,
R. B.
, 2007, “
Integration of Association Rules and Ontologies for Semantic Query Expansion
,”
Data Knowl. Eng.
0169-023X,
63
, pp.
63
75
.
36.
Conesa
,
J.
,
Storey
,
V. C.
, and
Sugumaran
,
V.
, 2008, “
Improving Web-Query Processing Through Semantic Knowledge
,”
Data Knowl. Eng.
0169-023X,
66
, pp.
18
34
.
37.
Gene Ontology Consortium
, 2001, “
Creating the Gene Ontology Resource: Design and Implementation
,”
Genome Res.
1088-9051,
11
(
8
), pp.
1425
1433
.
38.
39.
OpenCYC, URL, http://sw.opencyc.org/http://sw.opencyc.org/, last accessed 27/06/10.
44.
Bouchard
,
C.
,
Mougenot
,
C.
,
Omhover
,
J. F.
,
Setchi
,
R.
, and
Aoussat
,
A.
, 2007, “
Building a Domain Ontology for Designers, Towards a Kansei Based Ontology
,”
Proceedings of the Third I⋆PROMS International Conference
, Cardiff, UK, July 2–13, pp.
587
592
.
45.
Bouchard
,
C.
, and
Aoussat
,
A.
, 2003, “
Design Process Perceived as an Information Process to Enhance the Introduction of New Tools
,”
Int. J. Veh. Des.
0143-3369,
31
(
2
), pp.
162
175
.
46.
Büsher
,
M.
,
Frielaender
,
V.
,
Hodgson
,
E.
,
Rank
,
S.
, and
Shapiro
,
D.
, 2004, “
Design on Objects: Imaginative Practice, Aesthetic Categorisation, and the Design of Multimedia Archiving Support
,” Digital Creativity.
47.
Mougenot
,
C.
,
Bouchard
,
C.
, and
Aoussat
,
A.
, 2007. “
An Experimental Study of Designers’ Cognitive Activity in Design Information Phase
,”
ICED 2007, 16th International Conference on Engineering Design
, Paris, Aug. 28–3.
48.
Valette Florence
,
P.
, 1994, “
Introduction a L’analyse des Chaînages Cognitifs
,”
Recherche et Application en Marketing
,
9
(
1
), pp.
93
118
.
49.
Davidson
,
E.
, ed., 2003,
Roget’s Thesaurus of English Words and Phrases
,
Penguin
,
UK
.
50.
Jarmasz
,
M.
, and
Szpakowicz
,
S.
, 2001. “
Roget’s Thesaurus: A Lexical Resource to Treasure
,”
Proceedings of the NAACL WordNet and Other Lexical Resources Workshop
, Pittsburgh, pp.
186
188
.
51.
Cassidy
,
P.
, 2000, “
An Investigation of the Semantic Relations in the Roget’s Thesaurus: Preliminary Results
,”
CICLing-2000: Conference on Intelligent Text Processing and Computational Linguistics
,
A.
Gelbukh
ed.,
IPN Publishing House
,
Mexico City, Mexico
, pp.
181
204
; see http://www.cicling.org/2000/http://www.cicling.org/2000/.
52.
Morris
,
J.
, and
Hirst
,
G.
, 2004, “
Non-Classical Lexical Semantic Relations
,”
Workshop on Computational Lexical Semantics, Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics
, pp.
46
51
.
53.
Tang
,
Q.
, 2006, “
Knowledge Management Using Machine Learning, Natural Language Processing and Ontology
,” Ph.D. thesis, Trevithick Library, Cardiff University, UK.
55.
Salton
,
G.
, and
Buckley
,
C.
, 1990, “
Improving Retrieval Performance by Relevance Feedback
,”
J. Am. Soc. Inf. Sci.
0002-8231,
41
(
4
), pp.
288
297
.
56.
Gliozzo
,
A.
,
Strapparava
,
C.
, and
Dagan
,
I.
, 2004, “
Unsupervised and Supervised Exploitation of Semantic Domains in Lexical Disambiguation
,”
Comput. Speech Lang.
0885-2308,
18
(
3
), pp.
275
299
.
57.
OntoTag, 2008, Ontology Tagger, TRENDS Report, URL www.trendsproject.orgwww.trendsproject.org, last accessed 27/06/10.
58.
Copernic Desktop Search, URL http://www.copernic.com/en/store/http://www.copernic.com/en/store/, last accessed on 27/06/10.
59.
TRENDS, 2010, URL (http://www.trendsproject.org/http://www.trendsproject.org/) last accessed 27/06/10 (2010). TRENDS D7.2 Prototype 3 Testing Report.
You do not currently have access to this content.