Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Two biomedical sublanguages: a description based on the theories of Zellig Harris
Journal of Biomedical Informatics - Special issue: Sublanguage
Visualizing constraint-based temporal association rules
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Extracting information from free-text aircraft repair notes
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Languages and semantics of grammatical discrete structures
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Models of translational equivalence among words
Computational Linguistics
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Should we translate the documents or the queries in cross-language information retrieval?
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
A general feature space for automatic verb classification
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Information generation during design: Information importance and design effort
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Gene name ambiguity of eukaryotic nomenclatures
Bioinformatics
Using domain-specific verbs for term classification
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
A study of biologically-inspired design as a context for enhancing student innovation
FIE'09 Proceedings of the 39th IEEE international conference on Frontiers in education conference
Creative patterns and stimulation in conceptual design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A content account of creative analogies in biologically inspired design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A methodology for supporting “transfer” in biomimetic design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A natural-language approach to biomimetic design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Function-based, biologically inspired concept generation
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Model and algorithm for computer-aided inventive problem analysis
Computer-Aided Design
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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.