Biological Solutions for Engineering Problems: A Study in Cross-Domain Textual Case-Based Reasoning

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7969)


Textual Case-based Reasoning (TCBR) is a powerful paradigm within CBR. Biologically inspired design – the invention of technological systems by analogy to biological systems - presents an opportunity for exploring cross-domain TCBR. Our in situ studies of the retrieval task in biologically inspired design identified findability and recognizability of biology articles on the Web relevant to a design problem as major challenges. To address these challenges, we have developed a technique for semantic tagging of biology articles based on Structure-Behavior-Function models of the biological systems described in the article. We have also implemented the technique in an interactive system called Biologue. Controlled experiments with Biologue indicate improvements in both findability and recognizability of useful biology articles. Our work suggests that task-specific but domain-general model-based tagging might be useful for TCBR in support of complex reasoning tasks engaging cross-domain analogies.


Textual Document Retrieval Task Recognition Error Humpback Whale Engineer Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Benyus, J.: Biomimicry: Innovation Inspired by Nature. William Morrow (1997)Google Scholar
  2. 2.
    Biomimicry Institute (2008), Ask Nature – The Biomimicry Design Portal,
  3. 3.
    Burke, R., Hammond, K., Kulyukin, V., Lytinen, S., Tomuro, N., Schoenberg, S.: Question answering from frequently-asked questions files: experiences with the FAQ Finder system. AI Magazine 18(1), 57–66 (1997)Google Scholar
  4. 4.
    Brüninghaus, S., Ashley, K.: The role of information extraction for textual CBR. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 74–89. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  5. 5.
    Chakrabarti, A., Sarkar, P., Leelavathamma, B., Nataraju, B.: A functional representation for aiding biomimetic and artificial inspiration of new ideas. AIEDAM 19, 113–132 (2005)CrossRefGoogle Scholar
  6. 6.
    Chiu, I., Shu, L.: Biomimetic design through natural language analysis to facilitate cross-domain analysis. AIEDAM 21, 45–59 (2007)Google Scholar
  7. 7.
    Goel, A., Bhatta, S., Stroulia, E.: Kritik: An Early Case-Based Design System. In: Maher, Pu (eds.) Issues and Applications of Case-Based Reasoning in Design, pp. 87–132 (1997)Google Scholar
  8. 8.
    Goel, A., Mahesh, K., Peterson, J., Eiselt, K.: Unification of Language Understanding, Device Comprehension and Knowledge Acquisition. In: Proc. Cognitive Science Meeting 1996 (1996)Google Scholar
  9. 9.
    Goel, A., Rugaber, S., Vattam, S.: Structure, Behavior & Function of Complex Systems: The SBF Modeling Language. AIEDAM 23, 23–35 (2009)CrossRefGoogle Scholar
  10. 10.
    Goel, A., Vattam, S., Wiltgen, B., Helms, M.: Cognitive, collaborative, conceptual and creative - Four characteristics of the next generation of knowledge-based CAD systems: A study in biologically inspired design. Computer-Aided Design 44(10), 879–900 (2012)CrossRefGoogle Scholar
  11. 11.
    Helms, M., Vattam, S., Goel, A.: The Effects of Functional Modeling on Understanding Complex Biological Systems. In: Proc. 2010 ASME IDETC/CIE, Montreal, Canada (2010)Google Scholar
  12. 12.
    Holyoak, K., Thagard, P.: Analogical Retrieval by Constraint Satisfaction. Cognitive Science 13(3), 295–355 (1989)CrossRefGoogle Scholar
  13. 13.
    Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann Publishers, San Mateo (1993)Google Scholar
  14. 14.
    Lenz, M.: Defining knowledge layers for textual case-based reasoning. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS (LNAI), vol. 1488, pp. 298–309. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  15. 15.
    Nagel, J., Stone, R., McAdams, D.: An engineering-to-biology thesaurus for engineering design. In: Proc. ASME 2010 IDETC/CIE, Montreal, Canada (2010)Google Scholar
  16. 16.
    Peterson, J., Mahesh, K., Goel, A.: Situating Natural Language Understanding in Experience-Based Design. IJHCS 41, 881–913 (1994)Google Scholar
  17. 17.
    Pirolli, P.: Information foraging theory: Adaptive interaction with information. Oxford University Press, Oxford (2007)CrossRefGoogle Scholar
  18. 18.
    Raghunandan, M.A., Wiratunga, N., Chakraborti, S., Massie, S., Khemani, D.: Evaluation Measures for TCBR Systems. In: Althoff, K.-D., Bergmann, R., Minor, M., Hanft, A. (eds.) ECCBR 2008. LNCS (LNAI), vol. 5239, pp. 444–458. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Rissland, E., Daniels, J.: Using CBR to Drive IR. In: Procs. International Joint Conference on Artificial Intelligence, vol. 14, pp. 400–407 (1995)Google Scholar
  20. 20.
    Sen, S., Lam, S., Rashid, A., Cosley, D., Frankowski, D., Osterhouse, J., Harper, F., Riedl, J.: Tagging, communities, vocabulary, evolution. In: Procs. CSCW 2006, Banff, Canada, pp. 181–190 (2006)Google Scholar
  21. 21.
    Shu, L.H.: A natural-language approach to biomimetic design. AIEDAM 24(4), 483–505 (2010)CrossRefGoogle Scholar
  22. 22.
    Vattam, S.: Interactive Analogical Retrieval: Practice, Theory and Technology, Doctoral Dissertation, Georgia Institute of Technology (2012)Google Scholar
  23. 23.
    Vattam, S., Goel, A.: Foraging for inspiration: Understanding and supporting the information seeking practices of biologically inspired designers. In: Proc. ASME DETC Conference on Design Theory and Methods, Washington, DC (August 2011)Google Scholar
  24. 24.
    Vattam, S., Helms, M., Goel, A.: Compound Analogical Design: Interaction Between Problem Decomposition and Analogical Transfer in Biologically Inspired Design. In: Proc. DCC 2008, Atlanta, pp. 377–396. Springer (June 2008)Google Scholar
  25. 25.
    Weber, R., Ashley, K., Bruninghaus, S.: Textual Case-Based Reasoning. Knowledge Engineering Review 20(3), 255–260 (2006)CrossRefGoogle Scholar
  26. 26.
    Yen, J., Weissburg, M., Helms, M., Goel, A.: Biologically Inspired Design: ATool for Interdisciplinary Education. In: Bar-Cohen, Y. (ed.) Biomimetics: Nature-Based Innovation. Taylor & Francis (2011)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Design & Intelligence Lab, School of Interactive ComputingGeorgia Institute of TechnologyAtlantaUSA

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