Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-26T11:17:24.988Z Has data issue: false hasContentIssue false
  • English
  • Français

Biomimetic design through natural language analysis to facilitate cross-domain information retrieval

Published online by Cambridge University Press:  22 January 2007

IVEY CHIU  and
L.H. SHU
IVEY CHIU
Affiliation:
Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
L.H. SHU
Affiliation:
Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
Get access Rights & Permissions [Opens in a new window]

Abstract

Biomimetic, or biologically inspired, design uses analogous biological phenomena to develop solutions for engineering problems. Several instances of biomimetic design result from personal observations of biological phenomena. However, many engineers' knowledge of biology may be limited, thus reducing the potential of biologically inspired solutions. Our approach to biomimetic design takes advantage of the large amount of biological knowledge already available in books, journals, and so forth, by performing keyword searches on these existing natural-language sources. Because of the ambiguity and imprecision of natural language, challenges inherent to natural language processing were encountered. One challenge of retrieving relevant cross-domain information involves differences in domain vocabularies, or lexicons. A keyword meaningful to biologists may not occur to engineers. For an example problem that involved cleaning, that is, removing dirt, a biochemist suggested the keyword “defend.” Defend is not an obvious keyword to most engineers for this problem, nor are the words defend and “clean/remove” directly related within lexical references. However, previous work showed that biological phenomena retrieved by the keyword defend provided useful stimuli and produced successful concepts for the clean/remove problem. In this paper, we describe a method to systematically bridge the disparate biology and engineering domains using natural language analysis. For the clean/remove example, we were able to algorithmically generate several biologically meaningful keywords, including defend, that are not obviously related to the engineering problem. We developed a method to organize and rank the set of biologically meaningful keywords identified, and confirmed that we could achieve similar results for two other examples in encapsulation and microassembly. Although we specifically address cross-domain information retrieval from biology, the bridging process presented in this paper is not limited to biology, and can be used for any other domain given the availability of appropriate domain-specific knowledge sources and references.

Keywords

BiomimeticBiologically InspiredBioanalogous DesignConcept GenerationDomain LexiconsInformation Retrieval
Type
Research Article
Information
AI EDAM , Volume 21 , Issue 1: 20th Anniversary Issue , January 2007 , pp. 45 - 59
Copyright
© 2007 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Adamic, L.A., & Huberman, B.A. (2002). Zipf's law and the Internet. Glottometrics 3, 143–150.Google Scholar
Banerjee, S., & Pedersen, T. (2003). The design, implementation and use of the Ngram Statistics Package. Accessed at http://www.d.umn.edu/∼tpederse/nsp.html
Benami, O., & Jin, Y. (2002). Creative stimulation in conceptual design. Proc. ASME DETC/CIE, Paper No. DETC2002/DTM-34023, Montreal.
Biology Online. (2004). Accessed at www.biology-online.org
Biology Online. (2005). Accessed at www.biology-online.org
Burg, J.F.M. (1997). Linguistic instruments in requirements engineering. PhD Thesis. Amsterdam: Vrije Universiteit.
Chen, L., Liu, H., & Friedman, C. (2005). Gene name ambiguity of eukaryotic nomenclatures. Bioinformatics 21(2), 248–256.CrossRefGoogle Scholar
Cleveland, B., & Cleveland, A. (1990). Introduction to Indexing and Abstracting, 2nd ed. Englewood, CO: Libraries Unlimited.
Deerwester, S., Dumais, S.T., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the Society for Information Science 41(6), 391–407.3.0.CO;2-9>CrossRefGoogle Scholar
Dentsoras, A.J. (2005). Information generation during design: information importance and design effort. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19(1), 19–32.Google Scholar
Dong, A., Hill, A.W., & Agogino, A.M. (2003). A document analysis method for characterizing design team performance. Journal of Mechanical Design 126(3), 378–385.CrossRefGoogle Scholar
Dym, C., & Little, P. (2000). Engineering Design, A Project-Based Introduction. New York: Wiley.
Ehrenman, G. (2005). Mining what others miss. Mechanical Engineering 127(2), 26–31.CrossRefGoogle Scholar
Farley, B. (2001). Extracting information from free-text aircraft repair notes. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15(4), 295–305.CrossRefGoogle Scholar
Fellbaum, C. (1993). English verbs as a semantic net. Five Papers on WordNet, pp. 40–61. Accessed at ftp://ftp.cogsci.princeton.edu/pub/wordnet/5papers.ps
Friedman, C., Kra, P., & Rzhetsky, A. (2002). Two biomedical sublanguages: a description based on the theories of Zellig Harris. Journal of Biomedical Informatics 35, 222–235.CrossRefGoogle Scholar
Gero, J.S., Sushil, J.L., & Kundu, S. (1994). Evolutionary learning of novel grammars for design improvement. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 8(2), 83–94.Google Scholar
Gentner, D. (1983). Structure-mapping: a theoretical framework for analogy. Cognitive Science 7(2), 155–170.CrossRefGoogle Scholar
Gentner, D., & Holyoak, K.J. (1997). Reasoning and learning by analogy. American Psychologist 52(1), 32–34.CrossRefGoogle Scholar
Gordon, W.J.J. (1961). Synectics, The Development of Creative Capacity. New York: Harper & Row.
Hacco, E., & Shu, L.H. (2002). Biomimetic concept generation applied to design for remanufacture. Proc. ASME Design Engineering Technological Conf., Paper No. DETC2002/DFM-34177, Montreal, September 29–October 2.
Hine, R.S., & Martin, E., Eds. (2004). A Dictionary of Biology. New York: Oxford University Press.
Hirtz, J.M., Stone, R.B., McAdams, D.A., Szykman, S., & Wood, K.L. (2001). Evolving a functional basis for engineering design. Proc. ASME DETC/CIE, Paper No. DETC2001/DTM-21688, Pittsburgh, PA.
Hon, K.K.B., & Zeiner, J. (2004). Knowledge brokering for assisting the generation of automotive product design. Annals of the CIRP 53(1), 159–162.CrossRefGoogle Scholar
Iliopoulous, I., Enright, A.J., & Ouzounis, C.A. (2001). TextQuest: document clustering of Medline abstract for concept discovery in molecular biology. Pacific Symp. Biocomputing 2001, pp. 384–395.
Joanis, E., & Stevenson, S. (2003). A general feature space for automatic verb classification. Proc. 10th Conf. European ACL, pp. 163–170.CrossRef
Korpipaa, P. (2001). Visualizing constraint-based temporal association rules. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15(5), 401–410.Google Scholar
Korbel, J.O., Doerks, T., Jensen, L.J., Perez-Iratxeta, C., & Kaczanowski, S. (2005). Systematic association of genes to phenotypes by genome and literature mining. PLoS Biology 3(5), e134.CrossRefGoogle Scholar
Landauer, T.K., & Dumais, S.T. (1997). A solution to Plato's problem: the latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychology Review 104(2), 211–240.Google Scholar
Leech, G., Rayson, P., & Wilson, A. (2001). Companion Website for word frequencies in written and spoken English: based on British National Corpus. Accessed at www.comp.lancs.ac.uk/ucrel/bncfreq/
Li, X., & Schmidt, L. (2000). Grammar-based designer assistance for epicyclic gear trains. Proc. ASME DETC/CIE, Paper No. DETC2000/DTM-14574, Baltimore, MD.
Lindemann, U., & Gramann, J. (2004). Engineering design using biological principles. Proc. Int. Design Conf., Design 2004, Vol. 5, pp. 18–21, Dubrovnik.
Luhn, H.P. (1959). Auto-encoding of documents for information retrieval systems. In Modern Trends in Documentation, pp. 45–58. London: Pergamon Press.
Mabogunje, A., & Leifer, L. (1997). Noun phrases as surrogates for measuring early phases of the mechanical design process. Proc. ASME DETC/CIE, Sacramento, CA.
Mak, T.W., & Shu, L.H. (2004a). Abstraction of biological analogies for design. Annals of the CIRP 53(1), 117–120.Google Scholar
Mak, T.W., & Shu, L.H. (2004b). Use of biological phenomena in design by analogy. Proc. ASME DETC/CIE, Paper No. DETC2004/DETC-57303, Salt Lake City, UT.
Manser, M., Ed. (2004). The Chambers Thesaurus. Edinburgh: Chambers Harrap Publishers Ltd.
Matthews, P. (1997). The Concise Oxford Dictionary of Linguistics. New York: Oxford University Press.
McAdams, D., & Wood, K. (2000). Quantitative measures for design by analogy. Proc. ASME DETC/CIE, Paper No. DETC2000/DTM-14562, Baltimore, MD.
McCarley, J.S. (1999). Should we translate the documents or the queries in cross-language information retrieval? Proc. 37th Annual Meeting, pp. 208–214. College Park, MD: Association for Computational Linguistics.
Melamed, D.I. (2000). Models of translational equivalence among words. Computational Linguistics 26(1), 221–249.CrossRefGoogle Scholar
Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., & Miller, K. (1993). Introduction to WordNet: an on-line lexical database. Five Papers on WordNet, pp. 1–25. Accessed at ftp://ftp.cogsci.princeton.edu/pub/wordnet/5papers.ps
Purves, W.K., Sadava, D., Orians, G.H., & Heller, H.C. (2001). Life, The Science of Biology, 6/e. Sunderland, MA: Sinauer Associates.
Rebholz-Schuhmann, D., Kirsch, H., & Couto, F. (2005). Facts from text—is text mining ready to deliver? PLoS Biology 3(2), e65.Google Scholar
Resnik, P. (1997). Selectional preference and sense disambiguation. Proc. ACL SIGLEX Workshop on Tagging Text with Lexical Semantics: Why, What and How?, pp. 79–86, Somerset, NJ.
Segers, N. (2004). Computational representations of words and representations of words and associations in architectural design, development of a system supporting creative design. PhD Thesis. Technische Universiteit, Eindhoven.
Shea, K., & Cagan, J. (1999). Language and semantics of grammatical discrete structures. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13(4), 241–251.CrossRefGoogle Scholar
Shu, L., Lenau, T., Hansen, H., & Alting, L. (2003). Biomimetics applied to centering in microassembly. Annals of the CIRP 52(1), 101–104.CrossRefGoogle Scholar
Shu, L.H., Hansen, N.H., Gegeckaite, A., Moon, J., & Chan, C. (2006). Case study in biomimetic design: Handling and assembly of microparts. Proc. ASME IDETC/CIE, Paper No. DETC2006-99398, Philadelphia, PA.CrossRef
Spasic, I., Nenadic, G., & Ananiadou, S. (2003). Using domain-specific verbs for term classification. Proc. ACL Workshop on Natural Language Processing in Biomedicine, Sapporo, Japan, pp. 17–24.CrossRef
Sridharan, P., & Campbell, M.I. (2004). A grammar for function structures. Proc. ASME DETC/CIE, Paper No. DETC2004-57130, Salt Lake City, UT.CrossRef
Starling, A., & Shea, K. (2002). A clock grammar: the use of a parallel grammar in performance-based mechanical synthesis. Proc. ASME DETC/CIE, Paper No. DETC2002/DTM-34026, Montreal.
Stone, R.B., & Wood, K.L. (2000). Development of a functional basis for design. Journal of Mechanical Design, Transactions of the ASME 122, 359–369.CrossRefGoogle Scholar
Trask, R.L. (1999). Key Concepts in Language and Linguistics. London: Routledge.
Ullman, D. (2003). The Mechanical Design Process, 3rd ed. New York: McGraw–Hill.
Vakili, V., & Shu, L.H. (2001). Towards biomimetic concept generation. Proc. ASME DETC/CIE, Paper No. DETC2001/DTM-21715, Pittsburgh, PA.
Vincent, J., & Mann, D. (2002). Systematic technology transfer from biology to engineering. Philosophical Transactions of The Royal Society: Physical Sciences 360, 159–173.CrossRefGoogle Scholar
Waygood, E.B. (2003). Personal communication, Coordinator of Health Research, University of Saskatchewan.
Witten, I.H., & Frank, E. (2000). Data Mining, Practical Machine Learning Tools and Techniques with Java Implementations. San Francisco, CA: Morgan Kaufmann.
WordNet 2.0 (n.d.). Accessed at http://www.cogsci.princeton.edu/∼wn/
Yang, M.C., & Cutkosky, M.R. (1997). Automated indexing of design concepts for information management. Proc. Int. Conf. Engineering Design, Tampere, Finland, August 19–21.
Yang, M.C., Wood, W.H., & Cutkosky, M.R. (1998). Data mining for thesaurus generation in informal design information retrieval. Proc. 1998 Int. Congr. Civil Engineering, Boston, October 18–21.
Yarowsky, D. (1995). Unsupervised word-sense disambiguation rivalling supervised methods. Proc. 33rd Annual Meeting of the Association for Computational Linguistics, pp. 189–196.CrossRef
Yen, S., Fruchter, R., & Leifer, L. (1999). Facilitating tacit knowledge capture and reuse in conceptual design activities. Proc. ASME DETC/CIE, Paper No. DETC99/DTM-8781, Las Vegas, NV.
Yu, C., Cuadrudo, J., Ceglowski, M., & Payne, J. (2003). Patterns in Unstructured Data, Discovery, Aggregation and Visualization. Paper presented to the Andrew W. Mellon Foundation. Accessed at http://javelina.cet.middlebury.edu/lsa/out/lsa_explanation
Zipf, G.K. (1949). Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology. Cambridge, MA: Addison–Wesley.