Imperial College London

ProfessorPeterChilds

Faculty of EngineeringDyson School of Design Engineering

Chair and Leader in Engineering Design
 
 
 
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Contact

 

+44 (0)20 7594 7049p.childs Website CV

 
 
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Location

 

Studio 1, Dyson BuildingDyson BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Han:2018:10.1017/S0890060418000082,
author = {Han, J and Shi, F and Chen, L and Childs, PRN},
doi = {10.1017/S0890060418000082},
journal = {AI EDAM},
pages = {462--477},
title = {A computational tool for creative idea generation based on analogical reasoning and ontology},
url = {http://dx.doi.org/10.1017/S0890060418000082},
volume = {32},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Analogy is a core cognition process used to produce inferences as well as new ideas using previous knowledge and experience. Ontology is a formal representation of a set of domain concepts and their relationships. The use of analogy and ontology in design activities to support design creativity have previously been explored. This paper explores an approach to construct ontologies with sufficient richness and coverage to support reasoning over real-world datasets for prompting creative idea generation. This approach has been implemented into a computational tool for assisting designers in generating creative ideas during the early stages of design. The tool, called “the Retriever”, has been developed based on ontology by embracing the aspects of analogical reasoning. A case study has indicated that the tool can be effective and useful for idea generation. The results have indicated that the tool, in its current formulation, can significantly improve the fluency and flexibility of idea generation and the usefulness of ideas, as well as slightly increase the originality of ideas, for the case study concerned.
AU - Han,J
AU - Shi,F
AU - Chen,L
AU - Childs,PRN
DO - 10.1017/S0890060418000082
EP - 477
PY - 2018///
SN - 0890-0604
SP - 462
TI - A computational tool for creative idea generation based on analogical reasoning and ontology
T2 - AI EDAM
UR - http://dx.doi.org/10.1017/S0890060418000082
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000446673700008&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/64006
VL - 32
ER -