Building Emic-Based Cultural Mediations to Support Artificial Cultural Awareness

  • Jean Petit
  • Jean-Charles Boisson
  • Francis Rousseaux
Part of the Intelligent Systems Reference Library book series (ISRL, volume 134)


Recently, studies about culturally-intelligent systems have arisen to manage digitized cultural diversity. The current systems possess an artificial awareness of cultures by mediating them through representations. Coming from an etic approach, these universal representations facilitate the mediation of different cultures but limit their understanding and thus, prevent the development of an higher degree of awareness. In this research, we propose a methodology to construct artificial cultural awareness from emic-based representations. We tested the latter through an experiment on the domain of ‘abortion’ with the Pro-Choice and Pro-Life communities.


Culturally-Aware systems Culturally-intelligent systems Artificial cultural awareness Prototypical cultural models Cultural ontologies Cultural mediations 


  1. 1.
    Blanchard, E.G., Mizoguchi, R., Lajoie, S.P.: Structuring the cultural domain with an upper ontology of culture. The Handbook of Research on Culturally-Aware Information Technology: Perspectives and Models, pp. 179–212 (2010)Google Scholar
  2. 2.
    Rehm, M., Nakano, Y., André, E., Nishida, T., Bee, N., Endrass, B., Wissner, M., Lipi, A.A., Huang, H.-H.: From observation to simulation: generating culture-specific behavior for interactive systems. AI & Soc. 24(3), 267–280 (2009)CrossRefGoogle Scholar
  3. 3.
    Johnson, W.L.: Using immersive simulations to develop intercultural competence. In: Culture and Computing, pp. 1–15. Springer, Berlin (2010)Google Scholar
  4. 4.
    Tomalin, B., Stempleski, S.: Cultural Awareness. Oxford University Press, Oxford (2013)Google Scholar
  5. 5.
    National Center for Cultural Competence (2004)Google Scholar
  6. 6.
    Mohammed, P., Mohan, P.: Breakthroughs and challenges in culturally-aware technology enhanced learning. In: Proceedings of Workshop on Culturally-aware Technology Enhanced Learning in Conjuction with EC-TEL 2013, Paphos, Cyprus, 17 September 2013Google Scholar
  7. 7.
    Hofstede, G.H., Hofstede, G.: Culture’s Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations. Sage, Thousand Oaks (2001)Google Scholar
  8. 8.
    Hofstede, G., Bond, M.H.: Hofstede’s culture dimensions an independent validation using rokeach’s value survey. J. Cross Cult. Psychol. 15(4), 417–433 (1984)CrossRefGoogle Scholar
  9. 9.
    Khashman, N., Large, A.: Investigating the design of arabic web interfaces using hofstede’s cultural dimensions: a case study of government web portals. In: Proceedings of the Annual Conference of CAIS/Actes du congrès annuel de l’ACSI (2013)Google Scholar
  10. 10.
    Mascarenhas, S., Paiva, A.: Creating virtual synthetic cultures for intercultural training. In: Third International Workshop on Culturally-Aware Tutoring Systems (CATS2010), p. 25. Building Time-Affordable Cultural Ontologies using an Emic Approach 19 (2010)Google Scholar
  11. 11.
    Reinecke, K., Bernstein, A.: Tell me where you’ve lived, and i’ll tell you what you like: adapting interfaces to cultural preferences. In: International Conference on User Modeling, Adaptation, and Personalization, pp. 185–196. Springer, Berlin (2009)Google Scholar
  12. 12.
    Marcus, A., Gould, E.W.: Crosscurrents: cultural dimensions and global web user-interface design. Interactions 7(4), 32–46 (2000)Google Scholar
  13. 13.
    Chandramouli, K., Stewart, C., Brailsford, T., Izquierdo, E.: Cae-l: an ontology modeling cultural behaviour in adaptive education. In: Semantic Media Adaptation and Personalization, 2008. SMAP’08. Third International Workshop on, pp. 183–188, IEEE (2008)Google Scholar
  14. 14.
    Mohammed, P., Blanchard, E.G.: Leveraging comparisons between cultural frameworks: preliminary investigations of the mauoc ontological ecology. In: Sixth International Workshop on Culturally-Aware Tutoring Systems (CATS2015), p. 1 (2015)Google Scholar
  15. 15.
    Bennardo, G., De Munck, V.C.: Cultural Models: Genesis, Methods, and Experiences. Oxford University Press, Oxford (2014)Google Scholar
  16. 16.
    Kroeber, A.L., Parsons, T.: The concepts of culture and of social system. Am. Sociol. Rev. 23(5), 582–583 (1958)Google Scholar
  17. 17.
    Spencer-Oatey, H., Franklin, P.: What is culture. A compilation of quotations. GlobalPAD Core Concepts (2012)Google Scholar
  18. 18.
    Matsumoto, D.R.: Cultural Influences on Research Methods and Statistics. Brooks/Cole Publishing Company, USA (1994)Google Scholar
  19. 19.
    Pelto, P.J., Pelto, G.H.: intra-cultural diversity: some theoretical issues. Am. Ethnol. 2(1), 1–18 (1975)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Schein, E.H.: Coming to a new awareness of organizational culture. Sloan Manag. Rev. 25(2), 3–16 (1984)Google Scholar
  21. 21.
    Schein, E.: Organizational culture. Am. Psychol. (1990)Google Scholar
  22. 22.
    Ferraro, G.: The Cultural Dimension of International Business, 3rd edn (1998)Google Scholar
  23. 23.
    Mathews, H.F.: Uncovering cultural models of gender from accounts of folktales. In: Finding Culture in Talk, pp. 105–155. Springer, Berlin (2005)Google Scholar
  24. 24.
    D’andrade, R.: A cognitivist’s view of the units debate in cultural anthropology. Cross Cult Res 35(2), 242–257 (2001)Google Scholar
  25. 25.
    Bartlett, F.C.: Remembering: An Experimental and Social Study. Cambridge University, Cambridge (1932)Google Scholar
  26. 26.
    Turner, V.W.: The Forest of Symbols: Aspects of Ndembu Ritual, vol. 101. Cornell University Press, New York (1967)Google Scholar
  27. 27.
    Goodenough, W.H.: Culture, Language, and Society. Benjamin-Cummings Pub Co, San Francisco (1981)Google Scholar
  28. 28.
    Craik, K.: The Nature of Explanation (1943)Google Scholar
  29. 29.
    Jones, N., Ross, H., Lynam, T., Perez, P., Leitch, A.: Mental models: an interdisciplinary synthesis of theory and methods. Ecol. Soc. 16(1) (2011)Google Scholar
  30. 30.
    Moray, N.: Models of models of… mental models. In: Perspectives on the Human Controller, pp. 271–285 (1997)Google Scholar
  31. 31.
    Byrne, R.M.: The rational imagination (2005)Google Scholar
  32. 32.
    Forrester, J.: W. Industrial Dynamics, vol. 1, no. 961, pp. 1–464. MITPress, Cambridge Mass (1961)Google Scholar
  33. 33.
    Chi, M.T.: Three types of conceptual change: belief revision, mental model transformation, and categorical shift. In: International Handbook of Research on Conceptual Change, pp. 61–82 (2008)Google Scholar
  34. 34.
    Johnson-Laird, P.N.: Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness, No. 6. Harvard University Press, Cambridge (1983)Google Scholar
  35. 35.
    Gentner, D., Gentner, D.R.: Flowing waters or teeming crowds: mental models of electricity. Tech. Rep. DTIC Document (1982)Google Scholar
  36. 36.
    Bank, T.W.: Thinking with mental models (2015)Google Scholar
  37. 37.
    Wiig, K.: People-focused knowledge management. How effective decision making leads to corporate success (2004)Google Scholar
  38. 38.
    Rutherford, A., Wilson, J.R.: Models of mental models: an ergonomist psychologist dialogue. In: Selected papers of the 8th Interdisciplinary Workshop on Informatics and Psychology: Mental Models and Human-Computer Interaction 2, pp. 39–58. North-Holland Publishing Co., New York (1989)Google Scholar
  39. 39.
    Piaget, J., Cook, M.: The Origins of Intelligence in Children, vol. 8. International Universities Press, New York (1952)CrossRefGoogle Scholar
  40. 40.
    Doyle, J.K.: Measuring change in mental models of dynamic systems: an exploratory study. Ph.D. thesis, Department of Economics, Siena College (1998)Google Scholar
  41. 41.
    Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63(2), 81 (1956)CrossRefGoogle Scholar
  42. 42.
    Wagner, W., Hayes, N.: Everyday Discourse and Common Sense: The Theory of Social Representations. Palgrave Macmillan, UK (2005)Google Scholar
  43. 43.
    Gee, J.P.: Video games and embodiment. Games Cult. 3(3–4), 253–263 (2008)CrossRefGoogle Scholar
  44. 44.
    Holland, D., Quinn, N.: Cultural Models in Language and Thought. Cambridge University Press, Cambridge (1987)Google Scholar
  45. 45.
    D’Andrade, R.: A folk model of the mind (1987)Google Scholar
  46. 46.
    Mathevet, R., Etienne, M., Lynam, T., Calvet, C.: Water management in the camargue biosphere reserve: insights from comparative mental models analysis. Ecol. Soc. 16(1) (2011)Google Scholar
  47. 47.
    Eraut, M.: Non-formal learning and tacit knowledge in professional work. Br. J. Educ. Psychol. 70(1), 113–136 (2000)CrossRefGoogle Scholar
  48. 48.
    Berninger, K., Kneeshaw, D., Messier, C.: The role of cultural models in local perceptions of sfm–differences and similarities of interest groups from three boreal regions. J. Environ. Manage. 90(2), 740–751 (2009)CrossRefGoogle Scholar
  49. 49.
    Mathieu, J.E., Heffner, T.S., Goodwin, G.F., Cannon-Bowers, J.A., Salas, E.: Scaling the quality of teammates’ mental models: equifinality and normative comparisons. J. Organ. Behav. 26(1), 37–56 (2005)CrossRefGoogle Scholar
  50. 50.
    Stone-Jovicich, S., Lynam, T., Leitch, A., Jones, N.: Using consensus analysis to assess mental models about water use and management in the crocodile river catchment, South Africa. Ecol. Soc. 16(1) (2011)Google Scholar
  51. 51.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications, vol. 8. Cambridge university press, Cambridge (1994)Google Scholar
  52. 52.
    Mathieu, J.E., Rapp, T.L., Maynard, M.T., Mangos, P.M.: Interactive effects of team and task shared mental models as related to air traffic controllers’ collective efficacy and effectiveness. Hum. Perform. 23(1), 22–40 (2009)CrossRefGoogle Scholar
  53. 53.
    Young, J.C.: A model of illness treatment decisions in a tarascan town. Am. Ethnol. 7(1), 106–131 (1980)CrossRefMathSciNetGoogle Scholar
  54. 54.
    Vuillot, C., Coron, N., Calatayud, F., Sirami, C., Mathevet, R., Gibon, A.: Ways of farming and ways of thinking: do farmers’ mental models of the landscape relate to their land management practices? Ecol. Soc. 21(1), 1–23 (2016)CrossRefGoogle Scholar
  55. 55.
    Polanyi, M.: Personal Knowledge: Towards a Post-Critical Philosophy. University of Chicago Press, Chicago (1958)Google Scholar
  56. 56.
    Alexander, P.A., Schallert, D.L., Hare, V.C.: Coming to terms: how researchers in learning and literacy talk about knowledge. Rev Edu Res 61(3), 315–343 (1991)CrossRefGoogle Scholar
  57. 57.
    DeChurch, L.A., Mesmer-Magnus, J.R.: Measuring shared team mental models: a meta-analysis (2010)Google Scholar
  58. 58.
    Kearney, A.R., Kaplan, S.: Toward a methodology for the measurement of knowledge structures of ordinary people the conceptual content cognitive map (3 cm). Environ. Behav. 29(5), 579–617 (1997)CrossRefGoogle Scholar
  59. 59.
    Freeman, H., Romney, A., Ferreira-Pinto, J., Klein, R., Smith, T.: Guatemalan and us concepts of success and failure. Hum. Organ. 40(2), 140–145 (1981)CrossRefGoogle Scholar
  60. 60.
    Kempton, W., Boster, J.S., Hartley, J.A.: Environmental Values in American Culture. MIT Press, Cambridge (1996)Google Scholar
  61. 61.
    Oravecz, Z., Vandekerckhove, J., Batchelder, W.H.: Bayesian cultural consensus theory. Field Methods 26(3), 207–222 (2014)CrossRefGoogle Scholar
  62. 62.
    Romney, A.K., Weller, S.C., Batchelder, W.H.: Culture as consensus: a theory of culture and informant accuracy. Am. Anthropol. 88(2), 313–338 (1986)CrossRefGoogle Scholar
  63. 63.
    Romney, A.K., Batchelder, W.H., Weller, S.C.: Recent applications of cultural consensus theory. Am. Behav. Sci. 31(2), 163–177 (1987)CrossRefGoogle Scholar
  64. 64.
    Weller, S.C.: Cultural consensus theory: applications and frequently asked questions. Field Methods 19(4), 339–368 (2007)CrossRefGoogle Scholar
  65. 65.
    Garro, L.C.: Remembering what one knows and the construction of the past: a comparison of cultural consensus theory and cultural schema theory. Ethos 28(3), 275–319 (2000)CrossRefGoogle Scholar
  66. 66.
    Batchelder, W.H., Romney, A.K.: Test theory without an answer key. Psychometrika 53(1), 71–92 (1988)CrossRefMATHMathSciNetGoogle Scholar
  67. 67.
    Borgatti, S.P., Halgin, D.S.: 10 consensus analysis. A Companion to Cognitive Anthropology, p. 171 (2011)Google Scholar
  68. 68.
    Gatewood, J.B., Lowe, J.W.: Employee Perceptions of Credit Unions: Implications for Member Profitability. Filene Research Institute, Madison, WI (2008)Google Scholar
  69. 69.
    Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing? Int. J. Hum Comput. Stud. 43(5–6), 907–928 (1995)CrossRefGoogle Scholar
  70. 70.
    Borst, W.N.: Construction of Engineering Ontologies for Knowledge Sharing and Reuse. Universiteit Twente, Enschede (1997)Google Scholar
  71. 71.
    Studer, R., Benjamins, V.R., Fensel, D.: Knowledge engineering: principles and methods. Data Knowl. Eng. 25(1–2), 161–197 (1998)CrossRefMATHGoogle Scholar
  72. 72.
    Uschold, M., Gruninger, M.: Ontologies: principles, methods and applications. Knowl. Eng. Rev. 11(02), 93–136 (1996)CrossRefGoogle Scholar
  73. 73.
    Gòmez-Pérez, A., Benjamins, R.: Overview of knowledge sharing and reuse components: ontologies and problem-solving methods. IJCAI and the Scandinavian AI Societies. CEUR Workshop Proceedings (1999)Google Scholar
  74. 74.
    Fernàndez-Lòpez, M., Gòmez-Pérez, A., Juristo, N.: Methontology: From Ontological Art Towards Ontological Engineering (1997)Google Scholar
  75. 75.
    Pennacchiotti, M., Pantel, P.: Ontologizing semantic relations. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, pp. 793–800, Association for Computational Linguistics (2006)Google Scholar
  76. 76.
    Klein, M.: Combining and relating ontologies: an analysis of problems and solutions. In IJCAI-2001 Workshop on ontologies and information sharing, pp. 53–62, USA (2001)Google Scholar
  77. 77.
    Visser, P.R., Jones, D.M., Bench-Capon, T.J., Shave, M.J.: Assessing heterogeneity by classifying ontology mismatches In: Proceedings of the FOIS, vol. 98 (1998)Google Scholar
  78. 78.
    Noy, N.F., Musen, M.A., et al.: Algorithm and tool for automated ontology merging and alignment. In: Proceedings of the 17th National Conference on Artificial Intelligence (AAAI-00). Available as SMI technical report SMI-2000–0831 (2000)Google Scholar
  79. 79.
    Su, X., Gulla, J.A.: Semantic enrichment for ontology mapping. In: International Conference on Application of Natural Language to Information Systems, pp. 217–228. Springer, Berlin (2004)Google Scholar
  80. 80.
    De Bruijn, J., Ehrig, M., Feier, C., Martìn-Recuerda, F., Scharffe, F., Weiten, M.: Ontology mediation, merging and aligning. Semantic web technologies, pp. 95–113 (2006)Google Scholar
  81. 81.
    Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25(1), 158–176 (2013)CrossRefGoogle Scholar
  82. 82.
    Calvanese, D., De Giacomo, G., Lenzerini, M.: Ontology of integration and integration of ontologies. Description Logics 49(10–19), 30 (2001)Google Scholar
  83. 83.
    Meilicke, C., Stuckenschmidt, H., Tamilin, A.: Repairing ontology mappings. In: AAAI, vol. 3, p. 6 (2007)Google Scholar
  84. 84.
    Calì, A., Lukasiewicz, T., Predoiu, L., Stuckenschmidt, H.: Tightly coupled probabilistic description logic programs for the semantic web. In: Journal on Data Semantics, pp. 95–130. Springer, Berlin (2009)Google Scholar
  85. 85.
    Granitzer, M., Sabol, V., Onn, K.W., Lukose, D., Tochtermann, K.: Ontology alignment—a survey with focus on visually supported semi-automatic techniques. Future Internet 2(3), 238–258 (2010)CrossRefGoogle Scholar
  86. 86.
    Kalfoglou, Y., Schorlemmer, M.: Ontology mapping: the state of the art. Knowl. Eng. Rev. 18(01), 1–31 (2003)CrossRefMATHGoogle Scholar
  87. 87.
    Maedche, A., Motik, B., Silva, N., Volz, R.: Mafra—a mapping framework for distributed ontologies. In: International Conference on Knowledge Engineering and Knowledge Management, pp. 235–250. Springer, Berlin (2002)Google Scholar
  88. 88.
    Su, X., Gulla, J.A.: An information retrieval approach to ontology mapping. Data Knowl. Eng. 58(1), 47–69 (2006)CrossRefGoogle Scholar
  89. 89.
    Carley, K., Palmquist, M.: Extracting, representing, and analyzing mental models. Soc. Forces, 601–636 (1992)Google Scholar
  90. 90.
    Shuyo, N.: Language detection library for java, vol. 7, p. 2016. Retrieved July 2010Google Scholar
  91. 91.
    Harris, Z.S.: Distributional structure. Word 10(2–3), 146–162 (1954)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Jean Petit
    • 1
  • Jean-Charles Boisson
    • 2
  • Francis Rousseaux
    • 3
  1. 1.Capgemini Technology ServicesSuresnesFrance
  2. 2.CASH Team, CReSTIC Laboratory (EA 3804)University of Reims Champagne-ArdenneReimsFrance
  3. 3.MODECO Team, CReSTIC Laboratory (EA 3804)University of Reims Champagne-ArdenneReimsFrance

Personalised recommendations