Research Papers

Retrieving Causally Related Functions From Natural-Language Text for Biomimetic Design

[+] Author and Article Information
Hyunmin Cheong

Department of Mechanical
and Industrial Engineering,
University of Toronto,
5 King's College Road,
Toronto, ON M5S 3G8, Canada
e-mail: cheong@mie.utoronto.ca

L. H. Shu

Department of Mechanical
and Industrial Engineering,
University of Toronto,
5 King's College Road,
Toronto, ON M5S 3G8, Canada
e-mail: shu@mie.utoronto.ca

1Corresponding author.

Contributed by the Design Theory and Methodology Committee Division of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 2, 2013; final manuscript received April 3, 2014; published online June 2, 2014. Assoc. Editor: Janis Terpenny.

J. Mech. Des 136(8), 081008 (Jun 02, 2014) (10 pages) Paper No: MD-13-1389; doi: 10.1115/1.4027494 History: Received September 02, 2013; Revised April 03, 2014

Identifying biological analogies is a significant challenge in biomimetic (biologically inspired) design. This paper builds on our previous work on finding biological phenomena in natural-language text. Specifically, a rule-based computational technique is used to identify biological analogies that contain causal relations. Causally related functions describe how one function is enabled by another function, and support the transfer of functional structure from analogies to design solutions. The causal-relation retrieval method uses patterns of syntactic information that represent causally related functions in individual sentences, and scored F-measures of 0.73–0.85. In a user study, novice designers found that of the total search matches, proportionally more of the matches obtained with the causal-relation retrieval method were relevant to design problems than those obtained with a single verb-keyword search. In addition, matches obtained with the causal-relation retrieval method increased the likelihood of using functional association to develop design concepts. Finally, the causal-relation retrieval method enables automatic extraction of biological analogies at the sentence level from a large amount of natural-language sources, which could support other approaches to biologically inspired design that require the identification of interesting biological phenomena.

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Fig. 1

Flowchart of processes involved in developing the causal-relation retrieval method, implemented in a search tool

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Fig. 2

Creation of experimental conditions

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Fig. 3

Number of relevant matches identified b/w control and enabling-function conditions. Error bars represent standard errors.

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Fig. 4

Normalized function association measure b/w control and enabling-function conditions. Error bars represent standard errors.



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