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Miscellany

Ontology-based systematization of functional knowledge

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Pages 327-351 | Received 01 Mar 2004, Published online: 22 Jan 2007
 

Abstract

It has been recognized that design knowledge is scattered around technology and target domains. One of the two major reasons for it is that different frameworks (viewpoints) for conceptualization of design knowledge are used when people try to describe knowledge in different domains. The other is that several key functional concepts are left undefined or even unidentified. In this paper, we first overview the state of the art of ontological engineering, which we believe is able to make a considerable contribution to resolving these difficulties. We then discuss our enterprise aiming at systematization of functional knowledge used for synthesis. We discuss ontologies that guide conceptualization of artefacts from the functional point of view. The framework for knowledge systematization is based on an extended device ontology and a functional concept ontology built on top of the extended device ontology. This paper particularly discusses the extended device ontology and its application in the mechanical domain. The utilization of the systematized functional knowledge in several application systems is also discussed, together with its advantages.

Acknowledgements

The authors would like to thank Dr. Toshinobu Kasai, Dr. Kouji Kozaki, Mr. Kouki Higashide, Mr. Toshio Ueda, Mr. Toshinobu Sano, Mr. Masaru Takahashi, Ms. Mariko Yoshikawa and Mr. Tomonobu Takahashi for their contributions to this work. Special thanks go to Dr. Masayoshi Fuse, Mr. Masakazu Kashiwase and Mr. Shuji Shinoki, Sumitomo Electric Industries, Ltd for their cooperation of the deployment. The authors are grateful to Mr Wilfred van der Vegte and Prof. Imre Horva´th, Delft University of Technology and to Prof. Mitsuru Ikeda for their valuable comments. This research is supported in part by the Japan Society for the Promotion of Science (JSPS-RFTF97P00701). Special thanks go to the members, Professors Eiji Arai, Masahiko Onosato and Hiroshi Kawakami.

Notes

Machine learning is not considered a method for knowledge accumulation. It is just to extract a bunch of knowledge at once that also suffers from knowledge accumulation when combined with knowledge learned by other learning methods.

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