Hostname: page-component-7dd5485656-c8zdz Total loading time: 0 Render date: 2025-10-30T15:37:35.994Z Has data issue: false hasContentIssue false
  • English
  • Français

Idea generation with Technology Semantic Network

Published online by Cambridge University Press:  03 March 2021

Serhad Sarica Open the ORCID record for Serhad Sarica [Opens in a new window] ,
Binyang Song ,
Jianxi Luo  and
Kristin L. Wood
Serhad Sarica*
Affiliation:
Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore
Binyang Song
Affiliation:
School of Engineering Design, Technology and Professional Programs, Pennsylvania State University, University Park, PA, USA
Jianxi Luo
Affiliation:
Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore SUTD-MIT International Design Centre, Singapore University of Technology and Design, Singapore
Kristin L. Wood
Affiliation:
Mechanical Engineering Department, College of Engineering, Design and Computing, University of Colorado Denver, Denver, CO, USA
*
Author for correspondence: Serhad Sarica, E-mail: serhad_sarica@mymail.sutd.edu.sg
Get access Rights & Permissions [Opens in a new window]

Abstract

There are growing efforts to mine public and common-sense semantic network databases for engineering design ideation stimuli. However, there is still a lack of design ideation aids based on semantic network databases that are specialized in engineering or technology-based knowledge. In this study, we present a new methodology of using the Technology Semantic Network (TechNet) to stimulate idea generation in engineering design. The core of the methodology is to guide the inference of new technical concepts in the white space surrounding a focal design domain according to their semantic distance in the large TechNet, for potential syntheses into new design ideas. We demonstrate the effectiveness in general, and use strategies and ideation outcome implications of the methodology via a case study of flying car design idea generation.

Keywords

Data-driven designflying caridea generationsemantic distanceTechnology Semantic Network

Information

Type
Research Article
Information
AI EDAM , Volume 35 , Issue 3 , August 2021 , pp. 265 - 283
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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.)

Article purchase

Temporarily unavailable

References

Alstott, J, Triulzi, G, Yan, B and Luo, J (2017) Mapping technology space by normalizing patent networks. Scientometrics 110, 443–479.CrossRefGoogle Scholar
Altshuller, G and Altov, H (1996) And Suddenly the Inventor Appeared: TRIZ, the Theory of Inventive Problem Solving. Worchester, MA: Technical Innovation Center, Inc.Google Scholar
Asche, G (2017) “80% of technical information found only in patents” – Is there proof of this? World Patent Information 48, 16–28.CrossRefGoogle Scholar
Benson, CL and Magee, CL (2013) A hybrid keyword and patent class methodology for selecting relevant sets of patents for a technological field. Scientometrics 96, 69–82.CrossRefGoogle Scholar
Berduygina, D and Cavallucci, D (2020) Improvement of automatic extraction of inventive information with patent claims structure recognition. In Proceedings of the 2020 Computing Conference, Vol. 2. Springer International Publishing, pp. 625–637.Google Scholar
Bollacker, K, Evans, C, Paritosh, P, Sturge, T and Taylor, J (2008) Freebase: a collaboratively created graph database for structuring human knowledge. 2008 ACM SIGMOD International Conference on Management of Data. ACM, pp. 1247–1249.CrossRefGoogle Scholar
Bridgman, L (1952) Jane's All the World's Aircraft 1951–1952. London: Sampson Low, Marston & Company, Ltd.Google Scholar
Camburn, B, He, Y, Raviselvam, S, Luo, J and Wood, K (2020) Machine learning-based design concept evaluation. Journal of Mechanical Design 142, 1–15.CrossRefGoogle Scholar
Chakrabarti, A and Tang, MXI (1996) Generating conceptual solutions on funcSION: evolution of a functional synthesiser. In Artificial Intelligence in Design '96. Dordrecht: Springer Netherlands, pp. 603–622.CrossRefGoogle Scholar
Chakrabarti, A, Sarkar, P, Leelavathamma, B and Nataraju, BS (2006) A functional representation for aiding biomimetic and artificial inspiration of new ideas. Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM 19, 113–132.Google Scholar
Chan, J and Schunn, C (2015) The impact of analogies on creative concept generation: lessons from an in vivo study in engineering design. Cognitive Science 39, 126–155.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Chen, T-J and Krishnamurthy, VR (2020) Investigating a mixed-initiative workflow for digital mind-mapping. Journal of Mechanical Design 142. doi:10.1115/1.4046808CrossRefGoogle Scholar
Chen, L, Wang, P, Dong, H, Shi, F, Han, J, Guo, Y, Childs, P, Xiao, J and Wu, C (2019) An artificial intelligence based data-driven approach for design ideation. Journal of Visual Communication and Image Representation 61, 10–22.CrossRefGoogle Scholar
Christensen, BT and Schunn, CD (2007) The relationship of analogical distance to analogical function and preinventive structure: the case of engineering design. Memory and Cognition 35, 29–38.CrossRefGoogle ScholarPubMed
Crow, SC (1997) A practical flying car. In 1997 World Aviation Congress. Reston, Virigina: American Institute of Aeronautics and Astronautics. doi:10.2514/6.1997-5582.CrossRefGoogle Scholar
Deldin, J and Schuknecht, M (2014) The AskNature database: enabling solutions in biomimetic design. In Goel, AK, McAdams, DA and Stone, RB (eds), Biologically Inspired Design. London: Springer, pp. 17–27.CrossRefGoogle Scholar
English, K, Naim, A, Lewis, K, Schmidt, S, Viswanathan, V, Linsey, J, McAdams, D, Bishop, B, Campbell, M, Popna, K, Stone, R and Orsborn, S (2010) Impacting designer creativity through IT-enabled concept generation. Journal of Computing and Information Science in Engineering 10, 1–10.CrossRefGoogle Scholar
Follmann, Z and da Cunha, AM (1997) Triphibian flying car design. In 1997 World Aviation Congress. Reston, Virigina: American Institute of Aeronautics and Astronautics. doi:10.2514/6.1997-5601.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Gartner Identifies Five Emerging Technology Trends That Will Blur the Lines Between Human and Machine (2018) Retrieved December 18, 2018, from https://www.gartner.com/en/newsroom/press-releases/2018-08-20-gartner-identifies-five-emerging-technology-trends-that-will-blur-the-lines-between-human-and-machineGoogle Scholar
Gentner, D and Markman, AB (1997) Structure mapping in analogy and similarity. American Psychologist 52, 45–56.CrossRefGoogle Scholar
Georgiev, GV and Georgiev, DD (2018) Enhancing user creativity: semantic measures for idea generation. Knowledge-Based Systems 151, 1–15.CrossRefGoogle Scholar
Gick, ML and Holyoak, KJ (1980) Analogical problem solving. Cognitive Psychology 12, 306–355.CrossRefGoogle Scholar
Goucher-Lambert, K and Cagan, J (2019) Crowdsourcing inspiration: using crowd generated inspirational stimuli to support designer ideation. Design Studies 61, 1–29.CrossRefGoogle Scholar
Government of Spain (2004) Patents as a Source of Technological Information in the Technology Transfer Process. Geneva: World Intellectual Property Organization.Google Scholar
Han, J, Forbes, H, Shi, F, Hao, J and Schaefer, D (2020 a) A data-driven approach for creative concept generation and evaluation. Proceedings of the Design Society: DESIGN Conference 1, 167–176.CrossRefGoogle Scholar
Han, J, Sarica, S, Shi, F and Luo, J (2020 b) Semantic Networks for Engineering Design: A Survey. Retrieved from: http://arxiv.org/abs/2012.07060Google Scholar
Harding, S (1998) Flying Jeeps: The U.S. Army's Search for the Ultimate “Vehicle.” Air Enthusiast (Jan–Feb 1998), pp. 10–12.Google Scholar
He, Y and Luo, J (2017) The novelty ‘sweet spot’ of invention. Design Science 3, e21.CrossRefGoogle Scholar
Jensen, E (1971) Fly now, drive later. Air Progress (Nov 1971).Google Scholar
Keshwani, S and Chakrabarti, A (2017) Influence of analogical domains and comprehensiveness in explanation of analogy on the novelty of designs. Research in Engineering Design 28, 381–410.CrossRefGoogle Scholar
Kim, K, Hwang, K and Kim, H (2013) Study of an adaptive fuzzy algorithm to control a rectangular-shaped unmanned surveillance flying car. Journal of Mechanical Science and Technology 27, 2477–2486.CrossRefGoogle Scholar
Lee, C, Kang, B and Shin, J (2015) Novelty-focused patent mapping for technology opportunity analysis. Technological Forecasting and Social Change 90, 355–365.CrossRefGoogle 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, 1–12.CrossRefGoogle Scholar
Liu, L, Li, Y, Xiong, Y and Cavallucci, D (2020 a) A new function-based patent knowledge retrieval tool for conceptual design of innovative products. Computers in Industry 115, 103154.CrossRefGoogle Scholar
Liu, Q, Wang, K, Li, Y and Liu, Y (2020 b) Data-driven concept network for inspiring designers’ idea generation. Journal of Computing and Information Science in Engineering 20(3), 1–39.Google Scholar
Luo, J, Song, B, Blessing, L and Wood, K (2018) Design opportunity conception using the total technology space map. Artificial Intelligence for Engineering Design Analysis and Manufacturing: AIEDAM 32, 449–461.CrossRefGoogle Scholar
Luo, J, Sarica, S and Wood, KL (2019) Computer-aided design ideation using InnoGPS. Volume 2A: 45th Design Automation Conference, Vol. 2A-2019. American Society of Mechanical Engineers.CrossRefGoogle Scholar
Miller, GA, Beckwith, R, Fellbaum, C, Gross, D and Miller, KJ (1990) Introduction to WordNet: an on-line lexical database. International Journal of Lexicography 3, 235–244.CrossRefGoogle Scholar
Mitchell, T, Cohen, W, Hruschka, E, Talukdar, P, Yang, B, Betteridge, A, Carlson, A, Dalvi, B, Gardner, M, Kisiel, B, Krishnamurthy, J, Lao, N, Mazaitis, K, Mohamed, T, Nakashole, N, Platanios, E, Ritter, A, Samadi, M, Settles, B, Wang, R, Wijaya, D, Gupta, A, Chen, X, Saparov, A, Greaves, M and Welling, J (2018). Never-ending learning. Communications of the ACM 61, 103–115.CrossRefGoogle Scholar
Mukherjea, S, Bamba, B and Kankar, P (2005) Information retrieval and knowledge discovery utilizing a BioMedical patent semantic web. IEEE Transactions on Knowledge and Data Engineering 17, 1099–1110.CrossRefGoogle Scholar
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. Journal of Mechanical Design 136, 101102.CrossRefGoogle Scholar
Nomaguchi, Y, Kawahara, T, Shoda, K and Fujita, K (2019) Assessing concept novelty potential with lexical and distributional word similarity for innovative design. Proceedings of the International Conference on Engineering Design, ICED, pp. 1413–1422.CrossRefGoogle Scholar
Qian, L and Gero, JS (1996) Function–behavior–structure paths and their role in analogy-based design. Artificial Intelligence for Engineering Design Analysis and Manufacturing 10, 289–312.CrossRefGoogle Scholar
Rebele, T, Suchanek, F, Hoffart, J, Biega, J, Kuzey, E and Weikum, G (2016) YAGO: a multilingual knowledge base from Wikipedia, Wordnet, and Geonames. International Semantic Web Conference, pp. 1–8.CrossRefGoogle Scholar
Russo, D, Montecchi, T and Liu, Y (2012) Functional-based search for patent technology transfer. Volume 2: 32nd Computers and Information in Engineering Conference, Parts A and B. American Society of Mechanical Engineers, pp. 529–539.CrossRefGoogle Scholar
Sarica, S, Song, B, Low, E and Luo, J (2019 a) Engineering knowledge graph for keyword discovery in patent search. Proceedings of the Design Society: International Conference on Engineering Design, 1, pp. 2249–2258.CrossRefGoogle Scholar
Sarica, S, Song, B, Luo, J and Wood, K (2019 b) Technology knowledge graph for design exploration: application to designing the future of flying cars. 39th Computers and Information in Engineering Conference, Vol. 1. American Society of Mechanical Engineers.CrossRefGoogle Scholar
Sarica, S, Luo, J and Wood, KL (2020) TechNet: technology semantic network based on patent data. Expert Systems with Applications 142. doi:10.1016/j.eswa.2019.112995CrossRefGoogle 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.CrossRefGoogle Scholar
Singh, S (2017) Flying Cars Are Close To Moving From Fiction To Reality. Retrieved February 10, 2019, from https://www.forbes.com/sites/sarwantsingh/2017/06/05/flying-cars-from-fiction-to-reality/#ad366ac4b461Google Scholar
Song, B and Luo, J (2017) Mining patent precedents for data-driven design: the case of spherical rolling robots. Journal of Mechanical Design 139, 111420.CrossRefGoogle Scholar
Song, B, Srinivasan, V and Luo, J (2017 a) Patent stimuli search and its influence on ideation outcomes. Design Science 3, e25.CrossRefGoogle Scholar
Song, K, Kim, KS and Lee, S (2017 b) Discovering new technology opportunities based on patents: text-mining and F-term analysis. Technovation 60–61, 1–14.CrossRefGoogle Scholar
Speer, R, Chin, J and Havasi, C (2017) ConceptNet 5.5: an open multilingual graph of general knowledge. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). Retrieved from: http://arxiv.org/abs/1612.03975Google Scholar
Srinivasan, V, Song, BY, Luo, JX, Subburaj, K, Elara, MR, Blessing, L and Wood, K (2018) Does analogical distance affect performance of ideation? Journal of Mechanical Design 140, 071101.CrossRefGoogle Scholar
Stockbridge, FP (1927) Glenn Curtiss Sees: A Vision of Aviation’s Future. Popular Science, 32–33.Google Scholar
Stone, RB and Wood, KL (2000) Development of a functional basis for design. Journal of Mechanical Design 122, 359–370.CrossRefGoogle Scholar
Suarez, FF and Utterback, JM (1995) Dominant designs and the survival of firms. Strategic Management Journal 16, 415–430.CrossRefGoogle Scholar
Sun, G, Yao, S and Carretero, JA (2014) Comparing cognitive efficiency of experienced and inexperienced designers in conceptual design processes. Journal of Cognitive Engineering and Decision Making 8, 330–351.CrossRefGoogle Scholar
Tseng, I, Moss, J, Cagan, J and Kotovsky, K (2008) The role of timing and analogical similarity in the stimulation of idea generation in design. Design Studies 29, 203–221.CrossRefGoogle Scholar
Ward, TB (1998) Analogical distance and purpose in creative thought: mental leaps versus mental hops. In Holyoak, KJ Gentner, D and Kokinov, BN (eds.), Advances in Analogy Research: Integration of Theory and Data From the Cognitive, Computational, and Neural Sciences. Sofia: New Bulgarian University, pp. 221–230.Google Scholar
Weisberg, RW (2006) Creativity: Understanding Innovation in Problem Solving, Science, Invention, and the Arts. Hoboken, NJ: John Wiley & Sons Inc.Google Scholar
Yan, B and Luo, J (2017 a) Filtering patent maps for visualization of diversification paths of inventors and organizations. Journal of the Association for Information Science and Technology 68, 1551–1563.CrossRefGoogle Scholar
Yan, B and Luo, J (2017 b) Measuring technological distance for patent mapping. Journal of the Association for Information Science and Technology 68, 423–437.CrossRefGoogle Scholar
Yoon, J, Park, H, Seo, W, Lee, JM, Coh, By and Kim, J (2015) Technology opportunity discovery (TOD) from existing technologies and products: a function-based TOD framework. Technological Forecasting and Social Change 100, 153–167.CrossRefGoogle Scholar
Ziesloff, H (1957) The Roadable Airplane. EAA Experimenter (Feb 1957).Google Scholar