Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-23T06:18:40.545Z Has data issue: false hasContentIssue false
  • English
  • Français

A computational tool for creative idea generation based on analogical reasoning and ontology

Published online by Cambridge University Press:  05 October 2018

Ji Han Open the ORCID record for Ji Han [Opens in a new window] ,
Feng Shi ,
Liuqing Chen  and
Peter R.N. Childs
Ji Han*
Affiliation:
Dyson School of Design Engineering, Imperial College London, South Kensington, London, SW7 2AZ, UK
Feng Shi
Affiliation:
Dyson School of Design Engineering, Imperial College London, South Kensington, London, SW7 2AZ, UK
Liuqing Chen
Affiliation:
Dyson School of Design Engineering, Imperial College London, South Kensington, London, SW7 2AZ, UK
Peter R.N. Childs
Affiliation:
Dyson School of Design Engineering, Imperial College London, South Kensington, London, SW7 2AZ, UK
*
Author for correspondence: Ji Han, E-mail: j.han14@imperial.ac.uk.
Get access Rights & Permissions [Opens in a new window]

Abstract

Analogy is a core cognition process used to produce inferences as well as new ideas using previous knowledge and experience. Ontology is a formal representation of a set of domain concepts and their relationships. The use of analogy and ontology in design activities to support design creativity have previously been explored. This paper explores an approach to construct ontologies with sufficient richness and coverage to support reasoning over real-world datasets for prompting creative idea generation. This approach has been implemented into a computational tool for assisting designers in generating creative ideas during the early stages of design. The tool, called “the Retriever”, has been developed based on ontology by embracing the aspects of analogical reasoning. A case study has indicated that the tool can be effective and useful for idea generation. The results have indicated that the tool, in its current formulation, can significantly improve the fluency and flexibility of idea generation and the usefulness of ideas, as well as slightly increase the originality of ideas, for the case study concerned.

Keywords

Analogycreativitydesign creativityideationontology
Type
Research Article
Information
AI EDAM , Volume 32 , Special Issue 4: Design Creativity , November 2018 , pp. 462 - 477
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agarwal, B and Mittal, N (2016) Sentiment analysis using ConceptNet ontology and context information. In Hussain A and Cambria E (eds), Prominent Feature Extraction for Sentiment Analysis. Socio-Affective Computing. Cham: Springer International Publishing, pp. 63–75.Google Scholar
Ahmed, S (2005) Encouraging reuse of design knowledge: a method to index knowledge. Design Studies 26, 565–592.Google Scholar
Ahmed, S and Štorga, M (2009) Merged ontology for engineering design: contrasting empirical and theoretical approaches to develop engineering ontologies. Artificial Intelligence for Engineering Design Analysis and Manufacturing 23, 391–407.Google Scholar
Altshuller, GS (1984) Creativity as an Exact Science: The Theory of the Solution of Inventive Problems. Amsterdam, The Netherlands: Gordon and Breach Publishers.Google Scholar
Amabile, TM (1983) The Social Psychology of Creativity. New York, USA: Springer-Verlag.Google Scholar
Baddeley, AD, Eysenck, MW and Anderson, MC (2009) Memory. East Sussex, UK: Psychology Press.Google Scholar
Ball, LJ and Christensen, BT (2009) Analogical reasoning and mental simulation in design: two strategies linked to uncertainty resolution. Design Studies 30, 169–186.Google Scholar
Ball, LJ, Ormerod, TC and Morley, NJ (2004) Spontaneous analogising in engineering design: a comparative analysis of experts and novices. Design Studies 25, 495–508.Google Scholar
Bar, M (2007) The proactive brain: using analogies and associations to generate predictions. Trends in Cognitive Sciences 11, 280–289.Google Scholar
Benedek, M, Jauk, E, Fink, A, Koschutnig, K, Reishofer, G, Ebner, F and Neubauer, AC (2014) To create or to recall? Neural mechanisms underlying the generation of creative new ideas. NeuroImage 88, 125–133.Google Scholar
Bhatta, SR and Goel, AK (1996 a) From design experiences to generic mechanisms: model-based learning in analogical design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10, 131–136.Google Scholar
Bhatta, SR and Goel, AK (1996 b) Model-based design indexing and index learning in engineering design. Engineering Applications of Artificial Intelligence 9, 601–609.Google Scholar
Blanchette, I and Dunbar, K (2000) How analogies are generated: the roles of structural and superficial similarity. Memory & Cognition 28, 108–124.Google Scholar
Blanchette, I and Dunbar, K (2001) Analogy use in naturalistic settings: the influence of audience, emotion, and goals. Memory & Cognition 29, 730–735.Google Scholar
Boden, MA (2004) The Creative Mind: Myths and Mechanisms, vol. 2. London, UK: Routledge.Google Scholar
Boden, MA (2009) Computer models of creativity. AI Magazine 30, 23–34.Google Scholar
Carruthers, P (2011) Creative action in mind. Philosophical Psychology 24, 437–461.Google Scholar
Casakin, H (2004) Visual analogy as a cognitive strategy in the design process. Expert versus novice performance. Journal of Design Research 4, 197–217.Google Scholar
Casakin, H and Goldschmidt, G (1999) Expertise and the use of visual analogy: implications for design education. Design Studies 20, 153–175.Google Scholar
Cash, P and Štorga, M (2015) Multifaceted assessment of ideation: using networks to link ideation and design activity. Journal of Engineering Design 26, 391–415.Google Scholar
Chakrabarti, A, Sarkar, P, Leelavathamma, B and Nataraju, BS (2005) A functional representation for aiding biomimetic and artificial inspiration of new ideas. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19, 113–132.Google Scholar
Chakrabarti, A and Shu, LH (2010) Biologically inspired design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24, 453–454.Google Scholar
Chakrabarti, A, Siddharth, L, Dinakar, M, Panda, M, Palegar, N and Keshwani, S (2017) Idea inspire 3.0 – a tool for analogical design. In Chakrabarti, A. and Chakrabarti, D. (eds), Research Into Design for Communities, Volume 2: Proceedings of ICoRD 2017. Singapore: Springer, pp. 475–485.Google Scholar
Chakrabarti, S, Dom, B, Agrawal, R and Raghavan, P (1998) Scalable feature selection, classification and signature generation for organizing large text databases into hierarchical topic taxonomies. The VLDB Journal 7, 163–178.Google Scholar
Chan, J, Dow, SP and Schunn, CD (2015) Do the best design ideas (really) come from conceptually distant sources of inspiration?. Design Studies 36, 31–58.Google Scholar
Chan, J, Fu, K, Schunn, C, Cagan, J, Wood, K and Kotovsky, K (2011) On the benefits and pitfalls of analogies for innovative design: ideation performance based on analogical distance, commonness, and modality of examples. Journal of Mechanical Design 133, 081004–081011.Google Scholar
Chandrasegaran, SK, Ramani, K, Sriram, RD, Horváth, I, Bernard, A, Harik, RF and Gao, W (2013) The evolution, challenges, and future of knowledge representation in product design systems. Computer-Aided Design 45, 204–228.Google Scholar
Chang-Shing, L, Zhi-Wei, J and Lin-Kai, H (2005) A fuzzy ontology and its application to news summarization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 35, 859–880.Google Scholar
Childs, P and Fountain, R (2011) Commercivity. DS 69: Proceedings of E&PDE 2011, the 13th International Conference on Engineering and Product Design Education. London, UK, 08–09.09.2011.Google Scholar
Childs, PRN (2018) Mechanical Design Engineering Handbook. 2nd Edn. Oxford, UK: Butterworth-Heinemann.Google Scholar
Chiu, I and Shu, LH (2012) Investigating effects of oppositely related semantic stimuli on design concept creativity. Journal of Engineering Design 23, 271–296.Google Scholar
Christensen, BT and Schunn, CD (2007) The relationship of analogical distance to analogical function and preinventive structure: the case of engineering design. Memory & Cognition 35, 29–38.Google Scholar
Cowan, N (2008) What are the differences between long-term, short-term, and working memory? Progress in Brain Research 169, 323–338.Google Scholar
Cross, N (2011) Design Thinking: Understanding How Designers Think and Work. New York, USA: Berg Publishers.Google Scholar
Cross, V and Bathija, V (2009) Automatic ontology creation using adaptation. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24, 127–141.Google Scholar
Daly, SR, Seifert, CM, Yilmaz, S and Gonzalez, R (2016) Comparing ideation techniques for beginning designers. Journal of Mechanical Design 138, 101108–101112.Google Scholar
Darlington, MJ and Culley, SJ (2008) Investigating ontology development for engineering design support. Advanced Engineering Informatics 22, 112–134.Google Scholar
Daugherty, J and Mentzer, N (2008) Analogical reasoning in the engineering design process and technology education applications. Journal of Technology Education 19, 7–21.Google Scholar
Eckert, C and Stacey, M (2000) Sources of inspiration: a language of design. Design Studies 21, 523–538.Google Scholar
Falkenhainer, B, Forbus, KD and Gentner, D (1989) The structure-mapping engine: algorithm and examples. Artificial Intelligence 41, 1–63.Google Scholar
Forbus, KD, Mostek, T and Ferguson, R (2002) An analogy ontology for integrating analogical processing and first-principles reasoning. AAAI/IAAI 2002, 878–885.Google Scholar
Fu, K, Cagan, J, Kotovsky, K and Wood, K (2013 a) Discovering structure in design databases through functional and surface based mapping. Journal of Mechanical Design 135, 031006–031013.Google Scholar
Fu, K, Chan, J, Cagan, J, Kotovsky, K, Schunn, C and Wood, K (2013 b) The meaning of “near” and “far”: the impact of structuring design databases and the effect of distance of analogy on design output. Journal of Mechanical Design 135, 021007–021012.Google Scholar
Fu, K, Moreno, D, Yang, M and Wood, KL (2014) Bio-inspired design: an overview investigating open questions from the broader field of design-by-analogy. Journal of Mechanical Design 136, 111102–111118.Google Scholar
Gelman, A and Carlin, J (2014) Beyond power calculations: assessing type S (sign) and type M (magnitude) errors. Perspectives on Psychological Science 9, 641–651.Google Scholar
Gentner, D (1983) Structure-mapping: a theoretical framework for analogy. Cognitive Science 7, 155–170.Google Scholar
Gentner, D and Forbus, KD (2011) Computational models of analogy. Wiley Interdisciplinary Reviews: Cognitive Science 2, 266–276.Google Scholar
Gentner, D and Smith, LA (2012) Analogical reasoning. In Ramachandran, VS (ed.), Encyclopedia of Human Behavior. 2nd Edn. Oxford: Elsevier, pp. 48–49.Google Scholar
Gentner, D and Smith, LA (2013) Analogical learning and reasoning. In Reisberg, D (ed.), The Oxford Handbook of Cognitive Psychology. Oxford: Oxford University Press, pp. 668–681.Google Scholar
Georgiev, GV, Sumitani, N and Taura, T (2017) Methodology for creating new scenes through the use of thematic relations for innovative designs. International Journal of Design Creativity and Innovation 5, 78–94.Google Scholar
Gero, JS and Kannengiesser, U (2007) A function–behavior–structure ontology of processes. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, 379–391.Google Scholar
Gero, JS and Kannengiesser, U (2014) The function-behaviour-structure ontology of design. In Chakrabarti, A and Blessing, LTM (eds), An Anthology of Theories and Models of Design: Philosophy, Approaches and Empirical Explorations. London: Springer London, pp. 263–283.Google Scholar
Gilchrist, A (2003) Thesauri, taxonomies and ontologies – an etymological note. Journal of Documentation 59, 7–18.Google Scholar
Goel, A, Bhatta, S and Stroulia, E (1997) Kritik: an early case-based design system. Issues and Applications of Case-Based Reasoning in Design 1997, 87–132.Google Scholar
Goel, AK (1997) Design, analogy, and creativity. IEEE Expert: Intelligent Systems and Their Applications 12, 62–70.Google Scholar
Goel, AK and Bhatta, SR (2004) Use of design patterns in analogy-based design. Advanced Engineering Informatics 18, 85–94.Google Scholar
Goel, AK, Vattam, S, Wiltgen, B and Helms, M (2012) Cognitive, collaborative, conceptual and creative – four characteristics of the next generation of knowledge-based CAD systems: a study in biologically inspired design. Computer-Aided Design 44, 879–900.Google Scholar
Goldschmidt, G (2001) Visual analogy – a strategy for design reasoning and learning. In Eastman CM, McCracken WM and Newstetter WC (eds), Design Knowing and Learning: Cognition in Design Education. Oxford: Elsevier Science, pp. 199–219.Google Scholar
Goldschmidt, G (2011) Avoiding design fixation: transformation and abstraction in mapping from source to target. The Journal of Creative Behavior 45, 92–100.Google Scholar
Goldschmidt, G and Sever, AL (2011) Inspiring design ideas with texts. Design Studies 32, 139–155.Google Scholar
Goldschmidt, G and Tatsa, D (2005) How good are good ideas? Correlates of design creativity. Design Studies 26, 593–611.Google Scholar
Gonçalves, M, Cardoso, C and Badke-Schaub, P (2014) What inspires designers? Preferences on inspirational approaches during idea generation. Design Studies 35, 29–53.Google Scholar
Gruber, TR (1993) A translation approach to portable ontology specifications. Knowledge Acquisition 5, 199–220.Google Scholar
Guarino, N (1998) Formal ontology and information systems. Proceedings of FOIS. 81–97.Google Scholar
Gulla, JA and Brasethvik, T (2008) A Hybrid Approach to Ontology Relationship Learning. International Conference on Application of Natural Language to Information Systems. Springer, 79–90.Google Scholar
Gulla, JA, Brasethvik, T and Kvarv, GS (2009) Association rules and cosine similarities in ontology relationship learning. In Filipe, J and Cordeiro, J (eds), Enterprise Information Systems: 10th International Conference, ICEIS 2008, Barcelona, Spain, June 12-16, 2008, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 201–212.Google Scholar
Gust, H, Krumnack, U, Kühnberger, KU and Schwering, A (2008) Analogical reasoning: a core of cognition. KI – Zeitschrift Künstliche Intelligenz 1, 8–12.Google Scholar
Han, J, Park, D, Shi, F, Chen, L, Hua, M and Childs, PR (2017 a) Three driven approaches to combinational creativity: problem-, similarity- and inspiration-driven. Proceedings of the institution of mechanical engineers, part C: Journal of Mechanical Engineering Science. doi: 10.1177/0954406217750189.Google Scholar
Han, J, Shi, F, Chen, L and Childs, PRN (2017 b) The Analogy Retriever – an idea generation tool. DS 87-4 Proceedings of the 21st International Conference on Engineering Design (ICED 17) Vol 4: Design Methods and Tools. Vancouver, Canada.Google Scholar
Han, J, Shi, F, Chen, L and Childs, PRN (2018) The Combinator – a computer-based tool for creative idea generation based on a simulation approach. Design Science 4, E11.Google Scholar
Han, J, Shi, F and Childs, PRN (2016) The Combinator: A computer-based tool for idea generation. Proceedings of the Design 2016 14th International Design Conference. Dubrovnik, Croatia.Google Scholar
Hatchuel, A, Weil, B and Le Masson, P (2013) Towards an ontology of design: lessons from C–K design theory and forcing. Research in Engineering Design 24, 147–163.Google Scholar
Helms, M, Vattam, SS and Goel, AK (2009) Biologically inspired design: process and products. Design Studies 30, 606–622.Google Scholar
Howard, TJ, Culley, S and Dekoninck, EA (2011) Reuse of ideas and concepts for creative stimuli in engineering design. Journal of Engineering Design 22, 565–581.Google Scholar
Jarosz, AF and Wiley, J (2014) What are the odds? A practical guide to computing and reporting Bayes factors. The Journal of Problem Solving 7, 2.Google Scholar
Jonson, B (2005) Design ideation: the conceptual sketch in the digital age. Design Studies 26, 613–624.Google Scholar
Kaufman, JC and Beghetto, RA (2009) Beyond big and little: the four c model of creativity. Review of General Psychology 13, 1–12.Google Scholar
Keller, R, Eckert, CM and Clarkson, PJ (2009) Using an engineering change methodology to support conceptual design. Journal of Engineering Design 20, 571–587.Google Scholar
Keshavarz, M and Lee, Y (2012) Ontology matching by using ConceptNet. The Asia Pacific Industrial Engineering & Management Systems, 1917–1925.Google Scholar
Kim, M and Choi, K-S (2003) Access to structural similarity in the analogical problem solving of children. School Psychology International 24, 218–231.Google Scholar
Kitamura, Y and Mizoguchi, R (2004) Ontology-based systematization of functional knowledge. Journal of Engineering Design 15, 327–351.Google Scholar
Kokinov, B and French, RM (2003) Computational models of analogy-making. In Nadel, L (ed.), Encyclopedia of Cognitive Science. London: Nature Publishing Group, pp. 113–118.Google Scholar
Laing, S and Masoodian, M (2016) A study of the influence of visual imagery on graphic design ideation. Design Studies 45, 187–209.Google Scholar
Lee, J, Chae, H, Kim, C-H and Kim, K (2009) Design of product ontology architecture for collaborative enterprises. Expert Systems with Applications 36, 2300–2309.Google Scholar
Li, Z and Ramani, K (2007) Ontology-based design information extraction and retrieval. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, 137–154.Google Scholar
Li, Z, Raskin, V and Ramani, K (2008) Developing engineering ontology for information retrieval. Journal of Computing and Information Science in Engineering 8, 011003–011013.Google Scholar
Licato, J, Bringsjord, S and Govindarajulu, NS (2015) How models of creativity and analogy need to answer the tailorability concern. In Besold, TR, Schorlemmer, M and Smaill, A (eds), Computational Creativity Research: Towards Creative Machines. Amsterdam: Atlantis Press, pp. 93–107.Google Scholar
Liikkanen, LA and Perttula, M (2010) Inspiring design idea generation: insights from a memory-search perspective. Journal of Engineering Design 21, 545–560.Google Scholar
Lin, J, Fox, MS and Bilgic, T (1996) A requirement ontology for engineering design. Concurrent Engineering 4, 279–291.Google Scholar
Linsey, JS, Clauss, EF, Kurtoglu, T, Murphy, JT, Wood, KL and Markman, AB (2011) An experimental study of group idea generation techniques: understanding the roles of idea representation and viewing methods. Journal of Mechanical Design 133, 031008–031015.Google Scholar
Linsey, JS, Laux, JP, Clauss, E, Wood, KL and Markman, AB (2007) Increasing innovation: a trilogy of experiments towards a design-by-analogy method. ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 3, 145–159.Google Scholar
Linsey, JS, Markman, AB and Wood, KL (2012) Design by analogy: a study of the WordTree method for problem re-representation. Journal of Mechanical Design 134, 041009-041009–12.Google Scholar
Liu, H and Singh, P (2004) Conceptnet – a practical commonsense reasoning tool-kit. BT Technology Journal 22, 211–226.Google Scholar
Liu, Y, Lim, S CJ and Lee, WB (2013) Product family design through ontology-based faceted component analysis, selection, and optimization. Journal of Mechanical Design 135, 081007.Google Scholar
Lopez, R, Linsey, JS and Smith, SM (2011) Characterizing the effect of domain distance in design-by-analogy. Volume 9: 23rd International Conference on Design Theory and Methodology; 16th Design for Manufacturing and the Life Cycle Conference. ASME International.Google Scholar
Luis-Ferreira, F and Jardim-Goncalves, R (2013) Internet of persons and things inspired on brain models and neurophysiology. Computational Methods in Social Sciences 1, 52–64.Google Scholar
Maedche, A and Staab, S (2001) Ontology learning for the semantic web. IEEE Intelligent Systems 16, 72–79.Google Scholar
Malaga, RA (2000) The effect of stimulus modes and associative distance in individual creativity support systems. Decision Support Systems 29, 125–141.Google Scholar
McCaffrey, T (2016) A visual representation to quantitate, diagnose, and improve creativity in insight problem solving. The Journal of Creative Behavior 52, 52–65.Google Scholar
McCaffrey, T and Spector, L (2017) An approach to human–machine collaboration in innovation. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 32, 1–15.Google Scholar
McDonagh, D and Storer, I (2004) Mood boards as a design catalyst and resource: researching an under-researched area. The Design Journal 7, 16–31.Google Scholar
Miller, GA (1956) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review 63, 81–97.Google Scholar
Moreno, DP, Blessing, LT, Yang, MC, Hernández, AA and Wood, KL (2016) Overcoming design fixation: design by analogy studies and nonintuitive findings. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30, 185–199.Google Scholar
Moreno, DP, Hernández, AA, Yang, MC, Otto, KN, Hölttä-Otto, K, Linsey, JS, Wood, KL and Linden, A (2014) Fundamental studies in design-by-analogy: a focus on domain-knowledge experts and applications to transactional design problems. Design Studies 35, 232–272.Google Scholar
Mukherjee, S and Joshi, S (2013) Sentiment aggregation using ConceptNet ontology. International Joint Conference on Natural Language Processing (IJCNLP). 570–578.Google Scholar
Mumford, MD (2003) Taking stock in taking stock. Creativity Research Journal 15, 147–151.Google Scholar
Nagai, Y, Taura, T and Mukai, F (2009) Concept blending and dissimilarity: factors for creative concept generation process. Design Studies 30, 648–675.Google Scholar
Nanda, J, Simpson, TW, Kumara, SR and Shooter, SB (2005) A methodology for product family ontology development using formal concept analysis and web ontology language. Journal of Computing and Information Science in Engineering 6, 103–113.Google Scholar
Nessel, DD and Graham, JM (2007) Thinking Strategies for Student Achievement: Improving Learning across the Curriculum, K-12. vol. 2. California, Corwin Press.Google Scholar
Obitko, M, Snasel, V, Smid, J and Snasel, V (2004) Ontology design with formal concept analysis. CLA. 1377–1390.Google Scholar
Osborn, AF (1979) Applied Imagination: Principles and Procedures of Creative Problem-Solving. vol. 3. New York, USA: Charles Scribener's Sons.Google Scholar
Ozkan, O and Dogan, F (2013) Cognitive strategies of analogical reasoning in design: differences between expert and novice designers. Design Studies 34, 161–192.Google Scholar
Perttula, M and Sipilä, P (2007) The idea exposure paradigm in design idea generation. Journal of Engineering Design 18, 93–102.Google Scholar
Qian, L and Gero, JS (1992) A design support system using analogy. In Gero, JS and Sudweeks, F (eds), Artificial Intelligence in Design ‘92. Dordrecht: Springer Netherlands, pp. 795–813.Google Scholar
Raad, E and Evermann, J (2015) The role of analogy in ontology alignment: a study on LISA. Cognitive Systems Research 33, 1–16.Google Scholar
Richland, LE and Simms, N (2015) Analogy, higher order thinking, and education. Wiley Interdisciplinary Reviews: Cognitive Science 6, 177–192.Google Scholar
Sarkar, P and Chakrabarti, A (2011) Assessing design creativity. Design Studies 32, 348–383.Google Scholar
Setchi, R and Bouchard, C (2010) In search of design inspiration: a semantic-based approach. ASME Journal of Computing and Information Science in Engineering 10, 031006-031006-23.Google Scholar
Shah, JJ, Smith, SM and Vargas-Hernandez, N (2003) Metrics for measuring ideation effectiveness. Design Studies 24, 111–134.Google Scholar
Shi, F, Chen, L, Han, J and Childs, P (2017) A data-driven text mining and semantic network analysis for design information retrieval. Journal of Mechanical Design 139, 111402–111414.Google Scholar
Shi, L and Setchi, R (2013) Ontology-based personalised retrieval in support of reminiscence. Knowledge-Based Systems 45, 47–61.Google Scholar
Sim, SK and Duffy, AHB (2003) Towards an ontology of generic engineering design activities. Research in Engineering Design 14, 200–223.Google Scholar
Sowa, JF (1992) Semantic networks. In Shapiro, SC (ed.), Encyclopaedia of Artificial Intelligence. 2nd Edn. New York: John Wiley & Sons, pp. 1493–1511.Google Scholar
Speer, R and Havasi, C (2012) Representing general relational knowledge in ConceptNet 5. Proceedings of the Eight International Conference on Language Resources and Evaluation.Google Scholar
Stemler, SE (2004) A comparison of consensus, consistency, and measurement approaches to estimating interrater reliability. Practical Assessment, Research & Evaluation 9, 1–19.Google Scholar
Sternberg, RJ and Lubart, TI (1998) The concept of creativity: prospects and paradigms. In Sternberg, RJ (ed.), Handbook of Creativity. Cambridge: Cambridge University Press, pp. 3–15.Google Scholar
Štorga, M, Andreasen, MM and Marjanović, D (2010) The design ontology: foundation for the design knowledge exchange and management. Journal of Engineering Design 21, 427–454.Google Scholar
Sureka, A, Goyal, V, Correa, D and Mondal, A (2010) Generating domain-specific ontology from common-sense semantic network for target specific sentiment analysis. Proceedings of the fifth international conference of the Global WordNet Association. Mumbai, India. 1–8.Google Scholar
Taura, T, Yamamoto, E, Fasiha, MYN, Goka, M, Mukai, F, Nagai, Y and Nakashima, H (2012) Constructive simulation of creative concept generation process in design: a research method for difficult-to-observe design-thinking processes. Journal of Engineering Design 23, 297–321.Google Scholar
Vattam, S, Wiltgen, B, Helms, M, Goel, AK and Yen, J (2011) DANE: Fostering Creativity in and Through Biologically Inspired Design. London: Springer pp. 115–122.Google Scholar
Verhaegen, P-A, D'hondt, J, Vandevenne, D, Dewulf, S and Duflou, JR (2011) Identifying candidates for design-by-analogy. Computers in Industry 62, 446–459.Google Scholar
Viswanathan, V, Atilola, O, Esposito, N and Linsey, J (2014) A study on the role of physical models in the mitigation of design fixation. Journal of Engineering Design 25, 25–43.Google Scholar
Ward, TB (2011) Analogies. In Runco, MA and Pritzker, SR (eds), Encyclopedia of Creativity. 2nd Edn. London: Academic Press, pp. 40–45.Google Scholar
Ward, TB and Kolomyts, Y (2010) Cognition and creativity. In Kaufman, JC and Sternberg, RJ (eds), The Cambridge Handbook of Creativity. Cambridge, UK: The Cambridge University Press, pp. 93–112.Google Scholar
Wilson, JO, Rosen, D, Nelson, BA and Yen, J (2010) The effects of biological examples in idea generation. Design Studies 31, 169–186.Google Scholar
Yan, J, Forbus, K and Gentner, D (2003) A theory of rerepresentation in analogical matching. Proceedings of the 25th Annual Conference of the Cognitive Science Society, pp. 1265–1270.Google Scholar
Yan, Y and Childs, PRN (2015) Creativity tools selection for design engineers in idea generation. DS 80-8 Proceedings of the 20th International Conference on Engineering Design (ICED 15) Vol 8: Innovation and Creativity. Milan, Italy, 27–30.07.15.Google Scholar
Yilmaz, S, Daly, SR, Seifert, CM and Gonzalez, R (2016) Evidence-based design heuristics for idea generation. Design Studies 46, 95–124.Google Scholar