Multilingual Semantic Networks for Kansei Study

  • Hideyoshi Yanagisawa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 739)


Words are communication media to share a concept in a community. A word involving ambiguity represents multiple concepts depending on contexts. Such ambiguity causes misunderstanding between people having different contexts. On the other hands, a community uses words to obtain responses from target population, such as customers and participants. The word ambiguity causes misunderstanding between a community and a target population due to different contexts. A community dealing with multiple languages (e.g. multinationals) has a difficulty in translation if there are no words in a second language, all meanings of which do not correspond to all meanings of a word one wishes to translate. To deal with word ambiguity, I proposed a multilingual semantic networks (MLSN) framework. The MLSN is a graph where multiple language words, as nodes, are semantically linked through concepts. I implemented WordNet of English, Japanese, and French into a graph database as a MLSN. I applied MLSN to following two analysis. In the first analysis, I investigated the meanings of ambiguous words such as “kansei”, and their semantic relations with relevant words in the three languages. I found that there are no words corresponding to all meanings of those words in second languages. In the second analysis, I discussed how MLSN supports to select and translate a set of words used as evaluation descriptors. I analyzed ten positive emotion words from Geneva Emotion Wheel and their translation. I demonstrated how MLSN automatically finds translation mismatches and semantic independence between emotion descriptors.


Multilingual semantic network ambiguity translation difficulty 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Yanagisawa, H., Nakano, S., Murakami, T.: A Proposal of Kansei Database Framework and Kansei Modelling Methodology for the Delight Design Platform. Journal of Integrated Design and Process Science 1-12 (2016)Google Scholar
  2. 2.
    Yanagisawa, H.: Kansei quality in product design. Emotional engineering, pp. 289-310. Springer (2011)Google Scholar
  3. 3.
    De Saussure, F.: Course in general linguistics. Columbia University Press (2011)Google Scholar
  4. 4.
    Yanagisawa, H., Murakami, T., Noguchi, S., Ohtomi, K., Hosaka, R.: Quantification Method of Diverse Kansei Quality for Emotional Design: Application of Product Sound Design. In: ASME 2007 Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASME, (2007)Google Scholar
  5. 5.
    Levy, P.: Beyond kansei engineering: The emancipation of kansei design. International Journal of Design 7, (2013)Google Scholar
  6. 6.
    Miller, G.A.: WordNet: a lexical database for English. Communications of the ACM 38, 39-41 (1995)Google Scholar
  7. 7.
    Favart, C., Esquivel Elizondo, D., Gentner, A., Mahut, T.: The Kansei Design Approach at Toyota Motor Europe. In: Fukuda, S. (ed.) Emotional Engineering Volume 4, pp. 119-144. Springer International Publishing, Cham (2016)Google Scholar
  8. 8.
    Harada, A.: Definition of Kansei, Evaluation of Kansei 2. Report of Modeling the evaluation structure of KANSEI (1998)Google Scholar
  9. 9.
    Scherer, K.R.: What are emotions? And how can they be measured? Social science information 44, 695-729 (2005)Google Scholar
  10. 10.
    Fontaine, J.J., Scherer, K.R., Soriano, C.: Components of emotional meaning: A sourcebook. OUP Oxford (2013)Google Scholar
  11. 11.
    Van de Vijver, F.J.R., Leung, K.: Methods and data analysis for cross-cultural research. Sage (1997)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.The University of TokyoTokyoJapan

Personalised recommendations