Hostname: page-component-cb9f654ff-nr592 Total loading time: 0 Render date: 2025-08-22T23:01:59.409Z Has data issue: false hasContentIssue false
  • English
  • Français

SEMANTIC ANALYSIS OF ENGINEERING DESIGN CONVERSATIONS

Part of: Design Innovation, Information and Knowledge

Published online by Cambridge University Press:  11 June 2020

G. V. Georgiev  and
D. D. Georgiev
G. V. Georgiev*
Affiliation:
University of Oulu, Finland
D. D. Georgiev
Affiliation:
Institute for Advanced Study, Varna, Bulgaria
*
*georgi.georgiev@oulu.fi

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

To objectively and quantitatively study transcribed protocols of design conversations, we apply a semantic analysis approach based on dynamic semantic networks of nouns. We examined the applicability of the approach focused on a dynamic evaluation of the design problem solving process in engineering design educational settings. Using a case of real-world case, we show that the approach is able to determine the time dynamics of semantic factors such as level of abstraction, polysemy, information content, and quantify convergence/divergence in engineering design conversations.

Keywords

design cognitiondesign processhuman behavioursemantic analysisenginnering design conversations

Information

Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 1265 - 1274
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2020. Published by Cambridge University Press

References

Adams, R.S. (2015), “Design review conversations: The dataset”, In: Adams, R.S. and Siddiqui, J.A. (Eds.), Analyzing Design Review Conversations, Purdue University Press, West Lafayette, Indiana.Google Scholar
Adams, R.S. and Siddiqui, J.A. (2013), Purdue DTRS – Design Review Conversations Database, XRoads Technical Report, TR-01-13, Purdue University, West Lafayette, Indiana.Google Scholar
Blanchard, E., Harzallah, M. and Kuntz, P. (2008), “A generic framework for comparing semantic similarities on a subsumption hierarchy”, In: Ghallab, M., Spyropoulos, C.D., Fakotakis, N. and Avouris, N. (Eds.), ECAI 2008: 18th European Conference on Artificial Intelligence including Prestigious Applications of Intelligent Systems (PAIS 2008), IOS Press, Patras, Greece, pp. 20–24.Google Scholar
Boden, M.A. (2004), The Creative Mind: Myths and Mechanisms, 2nd ed., Routledge, London.CrossRefGoogle Scholar
Brophy, D.R. (2001), “Comparing the attributes, activities, and performance of divergent, convergent, and combination thinkers”, Creativity Research Journal, Vol. 13 No. 3-4, pp. 439–455. http://doi.org/10.1207/S15326934CRJ1334_20CrossRefGoogle Scholar
Cardoso, A., Veale, T. and Wiggins, G.A. (2009), “Converging on the Divergent: The History (and Future) of the International Joint Workshops in Computational Creativity”, AI Magazine, Vol. 30 No. 3, pp. 15–22.CrossRefGoogle Scholar
Diestel, R. (2017), Graph Theory, 5th ed., Springer, Berlin.CrossRefGoogle Scholar
Dong, A. (2009), The Language of Design: Theory and Computation, Springer, London.Google Scholar
Fellbaum, C. (1998), WordNet: An Electronic Lexical Database, Cambridge, The MIT Press, Massachusetts.CrossRefGoogle Scholar
Georgiev, G.V. and Casakin, H. (2019), “Semantic Measures for Enhancing Creativity in Design Education”, Proceedings of the Design Society: International Conference on Engineering Design, Vol. 1 No. 1, pp. 369–378. https://doi.org/10.1017/dsi.2019.40Google Scholar
Georgiev, G.V. and Georgiev, D.D. (2018), “Enhancing user creativity: semantic measures for idea generation”, Knowledge-Based Systems, Vol. 151, pp. 1–15. http://doi.org/10.1016/j.knosys.2018.03.016CrossRefGoogle Scholar
Georgiev, G.V. and Georgiev, D.D. (2019), “Semantic analysis approach to studying design problem solving”, Proceedings of the Design Society: International Conference on Engineering Design, Vol. 1 No. 1, pp. 1823–1832. http://doi.org/10.1017/dsi.2019.188Google Scholar
Georgiev, G.V., Nagai, Y. and Taura, T. (2008) “Method of design evaluation focused on relations of meanings for a successful design ”, In: Marjanović, D., Štorga, M., Pavkovic, N. and Bojcetic, N., (Eds.), 10th International Design Conference, DESIGN 2008, Dubrovnik, Croatia, May 19-22, 2008, The Design Society, pp. 1149–1158.Google Scholar
Georgiev, G.V., Nagai, Y. and Taura, T. (2010), “A method for the evaluation of meaning structures and its application in conceptual design”, Journal of Design Research, Vol. 8 No. 3, pp. 214–234. http://doi.org/10.1504/jdr.2010.032607CrossRefGoogle Scholar
Georgiev, G.V. and Taura, T. (2014) “Polysemy in design review conversations” In 10th Design Thinking Research Symposium, Purdue University, West Lafayette, Purdue University, Indiana.Google Scholar
Goldschmidt, G. (2014), Linkography: Unfolding the Design Process, Cambridge, MIT Press, Massachusetts.CrossRefGoogle Scholar
Guilford, J.P. (1957), “Creative abilities in the arts”, Psychological Review, Vol. 64 No. 2, pp. 110–118. http://doi.org/10.1037/h0048280CrossRefGoogle ScholarPubMed
Hartley, R.T. and Barnden, J.A. (1997), “Semantic networks: visualizations of knowledge”, Trends in Cognitive Sciences, Vol. 1 No. 5, pp. 169–175. http://doi.org/10.1016/S1364-6613(97)01057-7CrossRefGoogle ScholarPubMed
Hass, R.W. (2017), “Tracking the dynamics of divergent thinking via semantic distance: Analytic methods and theoretical implications”, Memory & Cognition, Vol. 45 No. 2, pp. 233–244. http://doi.org/10.3758/s13421-016-0659-yCrossRefGoogle ScholarPubMed
Hatcher, G. et al. (2018), “Using linkography to compare creative methods for group ideation”, Design Studies, Vol. 58, pp. 127–152. http://doi.org/10.1016/j.destud.2018.05.002CrossRefGoogle Scholar
Jeffries, K.K. (2017), “A CAT with caveats: is the Consensual Assessment Technique a reliable measure of graphic design creativity?”, International Journal of Design Creativity and Innovation, Vol. 5 No. 1-2, pp. 16–28. https://doi.org/10.1080/21650349.2015.1084893CrossRefGoogle Scholar
Kan, J.W.T. and Gero, J.S. (2017), Quantitative Methods for Studying Design Protocols, Springer, Dordrecht.CrossRefGoogle Scholar
Mabogunje, A. and Leifer, L.J. (1997), “Noun phrases as surrogates for measuring early phases of the mechanical design process”, In 1997 ASME Design Engineering Technical Conferences: DETC ‘97, Sacramento, California, 14-17 September 1997, American Society of Mechanical Engineers.Google Scholar
Nomaguchi, Y. et al. (2019), “Assessing concept novelty potential with lexical and distributional word similarity for innovative design”, Proceedings of the Design Society: International Conference on Engineering Design, Vol. 1 No. 1, pp. 1413–1422. http://doi.org/10.1017/dsi.2019.147Google Scholar
Oltețeanu, A.-M., Schöttner, M. and Schubert, S. (2019), “Computationally resurrecting the functional remote associates test using cognitive word associates and principles from a computational solver”, Knowledge-Based Systems, Vol. 168, pp. 1–9. http://doi.org/10.1016/j.knosys.2018.12.023CrossRefGoogle Scholar
Ravin, Y. and Leacock, C. (2000), Polysemy: Theoretical and Computational Approaches, Oxford University Press, Oxford.Google Scholar
Resnik, P. (1999), “Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language”, Journal of Artificial Intelligence Research, Vol. 11, pp. 95–130.CrossRefGoogle Scholar
Segers, N.M., de Vries, B. and Achten, H.H. (2005), “Do word graphs stimulate design?”, Design Studies, Vol. 26 No. 6, pp. 625–647. http://doi.org/10.1016/j.destud.2005.05.002CrossRefGoogle Scholar
Sowa, J.F. (1991), Principles of Semantic Networks: Explorations in the Representation of Knowledge, San Mateo, Morgan Kaufmann, California.Google Scholar
Taura, T. and Nagai, Y. (2013), Concept Generation for Design Creativity: A Systematized Theory and Methodology, Springer, London.CrossRefGoogle Scholar
Taura, T. et al. (2012), “Constructive simulation of creative concept generation process in design: a research method for difficult-to-observe design-thinking processes”, Journal of Engineering Design, Vol. 23 No. 4, pp. 297–321. http://doi.org/10.1080/09544828.2011.637191CrossRefGoogle Scholar
Wilkenfeld, M.J. and Ward, T.B. (2001), “Similarity and emergence in conceptual combination”, Journal of Memory and Language, Vol. 45 No. 1, pp. 21–38. http://doi.org/10.1006/jmla.2000.2772CrossRefGoogle Scholar
Yamamoto, E. et al. (2009), “Virtual modeling of concept generation process for understanding and enhancing the nature of design creativity”, In: Norell Bergendahl, M., Grimheden, M., Leifer, L., Skogstad, P. and Lindemann, U. (Eds.), 17th International Conference on Engineering Design, Vol. 2, Design Theory and Research Methodology, Palo Alto, CA, 24-27 August 2009, The Design Society, pp. 101–112.Google Scholar
Yilmaz, S. and Daly, S.R. (2016), “Feedback in concept development: Comparing design disciplines”, Design Studies, Vol. 45, pp. 137–158. http://doi.org/10.1016/j.destud.2015.12.008CrossRefGoogle Scholar
Yoruk, S. and Runco, M.A. (2014), “The neuroscience of divergent thinking”, Activitas Nervosa Superior, Vol. 56 No. 1, pp. 1–16. http://doi.org/10.1007/bf03379602Google Scholar