Aesthetic Knowledge Diagrams: Bridging Understanding and Communication

  • Tatiana Gavrilova
  • Dmitry Kudryavtsev
  • Elvira Grinberg
Part of the Knowledge Management and Organizational Learning book series (IAKM, volume 7)


Knowledge diagrams represent all substantial aspects of information involved in designing, codifying and representing company or domain knowledge assets. The chapter considers not only design but also the use of such maps including but not limited to facilitation of learning; eliciting, capturing, archiving and using expert knowledge; planning instruction; assessment of deep understandings; research planning; collaborative knowledge modelling; creation of knowledge portfolios; curriculum design; e-learning; and administrative and strategic planning and monitoring. Knowledge diagrams belong to the multidisciplinary fields of knowledge engineering (KE) and knowledge management (KM), bringing in concepts and methods from several computer science domains such as artificial intelligence, databases, expert systems, decision support systems and information systems. KE is strongly related to cognitive and social sciences and socio-cognitive engineering, where knowledge is considered to be produced by humans and structured according to mutual understanding of how human reasoning and logic work. Currently, KE is related to the construction of shared conceptual frameworks, often presented visually as knowledge diagrams. The chapter describes cognitive aspects of knowledge diagram design, using ideas coined by Gestalt psychology, involving good form and aesthetic perception. It is aimed at all researchers and practitioners interested in the use of knowledge diagrams, as outlined above.


Knowledge engineering Knowledge diagram Visualization Aesthetics 



This research was partially supported financially by Russian Science Foundation grant (project No. 17-07-00228 А).


  1. Alavi, M., & Leidner, D. (2001). Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107–136.Google Scholar
  2. Andersson, B., Bergholtz, M., Edirisuriya, A., Ilayperuma, T., Johannesson, P., Gordijn, J., et al. (2006). Towards a reference ontology for business models. Lecture Notes in Computer Science, 4215, 482–496.Google Scholar
  3. Battista, A. D., Eades, P., Tamassia, R., & Tollis, I. (1999). Graph drawing: Algorithms for the visualization of graphs. Upper Saddle River: Prentice Hall.Google Scholar
  4. Blackwell, A., & Engelhardt, Y. (2002). A meta-taxonomy for diagram research. In M. Anderson, B. Meyer, & P. Olivier (Eds.), Diagrammatic representation and reasoning (pp. 47–64). New York: Springer.Google Scholar
  5. Bolotnikova, E., Gavrilova, T., & Gorovoy, V. (2011). To one method of ontology evaluation. International Journal of Computer and Systems Sciences, 50(3), 448–461.Google Scholar
  6. Börner, K. (2011). Plug-and-play macroscopes. Communications of the ACM, 54(3), 60–69.Google Scholar
  7. Börner, K., & Chen, C. (2001). Visual interfaces to digital libraries - its past, present, and future. In Proceedings of the 1st ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL’01) (pp. 482–485), JCDL workshop.Google Scholar
  8. Boyack, K., & Klavans, R. (2014). Creation of a highly detailed, dynamic, global model and map of science. Journal of the Association for Information Science and Technology, 65(4), 670–685.Google Scholar
  9. Buergi, P., & Roos, J. (2003). Images of strategy. European Management Journal, 21(1), 69–78.Google Scholar
  10. Cawthon, N., & Moere, A. (2007). The effect of aesthetic on the usability of data visualization. In Information visualization, IV’07, IEEE 11th international conference, 2007 (pp. 637–648).Google Scholar
  11. Chen, C. (2005). Top 10 unresolved information visualization problems. IEEE Computer Graphics and Applications, 25(4), 12–16.Google Scholar
  12. Dettmer, H. W. (1997). Goldratt’s theory of constraints: A systems approach to continuous improvement. Milwaukee: ASQ Quality Press.Google Scholar
  13. Dicheva, D., Sosnovsky, S., Gavrilova, T., & Brusilovsky, P. (2005). Ontological web portal for educational ontologies. SW-EL’05: Applications of semantic web technologies for e-Learning (pp. 19–11).Google Scholar
  14. Dörk, M., Williamson, C., & Carpendale, S. (2009). Towards visual web search: Interactive query formulation and search result visualization. Madrid: WSSP.Google Scholar
  15. Eisenstadt, M., Domingue, J., Rajan, T., & Motta, E. (1990). Visual knowledge engineering. IEEE Transactions on Software Engineering, 16(10), 1164–1177.Google Scholar
  16. Eppler, M. (2003). The image of insight: The use of visual metaphors in the communication of knowledge. Proceedings of I-KNOW, 3, 2–4.Google Scholar
  17. Eppler, M. (2008). A process-based classification of knowledge maps and application examples. Knowledge and Process Management, 15(1), 59–71.Google Scholar
  18. Eppler, M., & Burkhard, R. (2005). Knowledge visualization. In D. G. Schwartz (Ed.), Encyclopedia of knowledge management (pp. 551–560). Hershey, PA: IGI.Google Scholar
  19. Eppler, M., & Burkhard, R. (2007). Visual representations in knowledge management: Framework and cases. Journal of Knowledge Management, 11(4), 112–122.Google Scholar
  20. Eppler, M., & Jianxin, G. (2008). Communicating with diagrams: How intuitive and cross-cultural are business graphics? Euro Asia Journal of Management, 18(35), 3–22.Google Scholar
  21. Eppler, M., Hoffmann, F., & Bresciani, S. (2011). New business models through collaborative idea generation. International Journal of Innovation Management, 15(6), 1323–1341.Google Scholar
  22. Few, S. (2006). Information dashboard design. Toronto, ON: O’Reilly Media.Google Scholar
  23. Frank, U. (2002). Multi-perspective enterprise modeling (Memo). Conceptual framework and modeling languages. In Proceedings of Hawaii International Conference on System Sciences (HICSS) (pp. 1258–1267).Google Scholar
  24. Gavrilova, T. (2003). Ontological approach to knowledge management in the development of corporate information systems. Journal News of Artificial Intelligence, 2, 2003.Google Scholar
  25. Gavrilova, T. (2010). Orchestrating ontologies for courseware design. In A. Tzanavari & N. Tsapatsoulis (Eds.), Affective, interactive and cognitive methods for E-learning design: Creating an optimal education experience (pp. 155–172). Hershey, PA: IGI Global.Google Scholar
  26. Gavrilova, T., & Voinov, A. (1998). Work in progress: Visual specification of knowledge bases. In A. Pasqual del Pobil, J. Mira, & M. Ali (Eds.), Tasks and methods in applied artificial intelligence (Vol. 1416, pp. 717–726). Berlin: Springer.Google Scholar
  27. Gibson, J. (2014). The ecological approach to visual perception (Classic ed.). Routledge: Taylor & Francis.Google Scholar
  28. Govareshki, M., Hosseini, S., & Taghinejad, R. (2017). Use of knowledge maps in collaborative networks management (case study: SSFR company). International Journal of Computer Science and Network Security, 17(9), 21.Google Scholar
  29. Grigoriev, L., & Kudryavtsev, D. (2011). Ontology-based business architecture engineering framework. Frontiers in Artificial Intelligence and Applications, 231, 233–252.Google Scholar
  30. Gruninger, M., & Fox, M. (1995). Methodology for the design and evaluation of ontologies. In Proceedings of IJCAI 1995, Workshop on basic ontological issues in knowledge sharing, Montreal (pp. 1–10).Google Scholar
  31. Guizzardi G., Pires L., van Sinderen M. (2006). Ontology-based evaluation and design of domain-specific visual modeling languages. Advances in information systems development. Springer, Boston, MA. 217–228.Google Scholar
  32. Harel D., & Rumpe B. (2000). Modeling languages: Syntax, semantics and all that stuff, Part I: The basic stuff. Technical report.Google Scholar
  33. Heidari, F., Loucopoulos, P., Brazier, F., & Barjis, J. (2013). A meta-meta-model for seven business process modeling languages. In Proceedings of 15th conference on business informatics (CBI) (pp. 216–221).Google Scholar
  34. Herman, B., Melanon, G., & Marshall, M. (2000). Graph visualization and navigation in information visualization: A survey. IEEE Transactions on Visualization and Computer Graphics, 6(1), 24–43.Google Scholar
  35. Herrmann, C. S., & Bosch, V. (2001). Gestalt perception modulates early visual processing. Neuroreport, 12(5), 901–904.Google Scholar
  36. Hinkelmann, K., Gerber, A., Karagiannis, D., Thoenssen, B., van der Merwe, A., & Woitsch, R. (2015). A new paradigm for the continuous alignment of business and IT: Combining enterprise architecture modelling and enterprise ontology. Computers in Industry., 79, 77–86.Google Scholar
  37. Huff, A. (1990). Mapping strategic thought. Hoboken: Wiley.Google Scholar
  38. Karagiannis, D., & Höfferer, P. (2006). Metamodeling as an integration concept. In Software and data technologies (pp. 37–50). Berlin: Springer.Google Scholar
  39. Kingston, J., & Macintosh, A. (2000). Knowledge management through multi-perspective modelling: Representing and distributing organizational memory. Knowledge-Based Systems, 13(2), 121–131.Google Scholar
  40. Kosslyn, S. (2006). Graph design for the eye and the mind. New York, NY: Oxford University Press.Google Scholar
  41. Koznov D. (2011). Process model of DSM solution development and evolution for small and medium-sized software companies. In Proceedings of 15th IEEE International Enterprise Distributed Object Computing Workshop, Helsinki (EDOC 2011) (pp. 85–92).Google Scholar
  42. Kudryavtsev, D., & Gavrilova, T. (2011). Diagrammatic knowledge modeling for managers: Ontology-based approach. In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (pp. 386–389).Google Scholar
  43. Kudryavtsev, D., & Gavrilova, T. (2017). From anarchy to system: A novel classification of visual knowledge codification techniques. Knowledge and Process Management, 24(1), 3–13.Google Scholar
  44. Lengler R., Eppler M. (2007). Towards a periodic table of visualization methods for management. In Proceedings of the Conference on Graphics and Visualization in Engineering (pp. 1–6).Google Scholar
  45. Lohse, G., Biolsi, K., Walker, N., & Rueter, H. (1994). A classification of visual representations. Communications of the ACM, 37(12), 36–49.Google Scholar
  46. Luchins, A., & Luchins, E. (1982). An introduction to the origins of Wertheimer’s Gestalt psychology. Gestalt Theory, 4(3–4), 145–171.Google Scholar
  47. Manovich, L. (2009, March). Cultural analytics: Visualising cultural patterns in the era of “more media”. Domus.Google Scholar
  48. Mayer, R., Painter, K., & deWitte, P. (1992). IDEF family of methods for concurrent engineering and business re-engineering applications. College Station, TX: Knowledge Based Systems.Google Scholar
  49. Miller, G. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81.Google Scholar
  50. Musen, M. (1992). Dimensions of knowledge sharing and reuse. Computers and Biomedical Research, 25(5), 435–467.Google Scholar
  51. Mutschke, P., Scharnhorst, A., Belkin, N., Skupin, A., & Mayr, P. (2017). Guest editors’ introduction to the special issue on knowledge maps and information retrieval. (KMIR). International Journal on Digital Libraries, 18(1), 1. Google Scholar
  52. Nicolini, D. (2007). Studying visual practices in construction. Building Research & Information., 35, 576–580.Google Scholar
  53. Ren, Y., Parvizi, A., Mellish, C., Pan, J., Van Deemter, K., & Stevens, R. (2014). Towards competency question-driven ontology authoring. In The semantic web: Trends and challenges (pp. 752–767). New York: Springer.Google Scholar
  54. Rumbaugh, J., Jacobson, I., & Booch, G. (2004). Unified modeling language reference manual. Boston, MA: Pearson Higher Education.Google Scholar
  55. Sandkuhl, K. (2014). Knowledge reuse: Survey of existing techniques and classification approach. In Business intelligence (Vol. 205, pp. 126–148). Berlin: Springer.Google Scholar
  56. Santucci, G. (2013). Visual analytics and information retrieval. In M. Agosti et al. (Eds.), Information retrieval meets information visualization (pp. 116–131). New York: Springer.Google Scholar
  57. Sarrafzadeh, B., Vtyurina, A., Lank, E., & Vechtomova, O. (2016). Knowledge graphs versus hierarchies: An analysis of user behaviours and perspectives in information seeking. In Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval (pp. 91–100).Google Scholar
  58. Shadbolt, N., & Milton, N. (1999). From knowledge engineering to knowledge management. British Journal of Management, 10(4), 309–322.Google Scholar
  59. Shiffrin, R., & Boerner, K. (2004). Mapping knowledge domains. Proceedings of the National Academy of Sciences of the USA, 101(1), 5183–5185.Google Scholar
  60. Skupin, A. (2004). The world of geography: Visualizing a knowledge domain with cartographic means. Proceedings of the National Academy of Sciences of the USA, 101(1), 5274–5278.Google Scholar
  61. Strakhovich, E. (2014). Ontological engineering in education: Tools for knowledge transfer and knowledge assessment. In Advanced learning technologies (ICALT), IEEE 14th International Conference (pp. 714–715).Google Scholar
  62. Studer, R., Benjamins, V. R., & Fensel, D. (1998). Knowledge engineering: Principles and methods. Data and Knowledge Engineering, 25(1), 161–198.Google Scholar
  63. Sun, D., & Wong, K. (2005). On evaluating the layout of UML class diagrams for program comprehension. In Program comprehension, IWPC, Proceedings, IEEE 13th International Workshop (pp. 317–326).Google Scholar
  64. Tangherlini, T. (2013). The folklore macroscope: Challenges for a computational folkloristics. The 34th Archer Taylor memorial lecture. Western Folklore, 72(1), 7–27.Google Scholar
  65. The Open Group. (2012). ArchiMate 2.1 specification. The Open Group Publications catalog, 2012–2013. Accessed February 23, 2016, from
  66. van Vijk, J. (2006). Views on visualization. IEEE Transaction on Visualization and Computer Graphics, 12, 421–432.Google Scholar
  67. Wei, F., Liu, S., Song, Y., Pan, S., Zhou, M., Qian, W., et al. (2010). TIARA: A visual exploratory text analytic system. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’10) (pp. 153–162).Google Scholar
  68. Wells, D. (1990). Are these the most beautiful? The Mathematical Intelligencer, 12, 37–41. Scholar
  69. Werthheimer, M. (1945). Productive thinking. New York: Harper Collins.Google Scholar
  70. Wielinga, B., Sandberg, J., & Schreiber, G. (1997). Methods and techniques for knowledge management: What has knowledge engineering to offer? Expert Systems with Applications, 13(1), 73–84.Google Scholar
  71. Yudelson, M., Gavrilova, T., & Brusilovsky, P. (2005). Towards user modeling meta-ontology. In International conference on user modeling, Springer, Berlin (pp. 448–452).Google Scholar
  72. Zachman, J. (2003). The Zachman framework for enterprise architecture: A primer for enterprise engineering and manufacturing. Monument, CO: Zachman International.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tatiana Gavrilova
    • 1
  • Dmitry Kudryavtsev
    • 1
  • Elvira Grinberg
    • 1
  1. 1.Graduate School of Management, Saint-Petersburg UniversitySaint-PetersburgRussia

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