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Developing a User Property Metadata to Support Cognitive and Emotional Product Design

인지·감성적 제품설계 지원을 위한 사용자 특성정보 메타데이터 구축

  • Oh, Kyuhyup (Department of Industrial & Management Systems Engineering, Kyung Hee University) ;
  • Park, Kwang Il (Department of Information & Industrial Engineering, Yonsei University) ;
  • Kim, Hee-Chan (Department of Mechanical Engineering, Yonsei University) ;
  • Kim, Woo Ju (Department of Information & Industrial Engineering, Yonsei University) ;
  • Lee, Soo-Hong (Department of Mechanical Engineering, Yonsei University) ;
  • Ji, Young Gu (Department of Information & Industrial Engineering, Yonsei University) ;
  • Jung, Jae-Yoon (Department of Industrial & Management Systems Engineering, Kyung Hee University)
  • Received : 2016.10.29
  • Accepted : 2016.11.22
  • Published : 2016.11.30

Abstract

Cognitive and emotional product design is becoming crucial because the technology gap decreases more and more. Product design guidelines and the corresponding database are therefore needed to support sensing (e.g. sight, hearing, touch), cognition (e.g. attention, memory) and emotion (e.g. aesthetics, functionality) which users feel differently according to their genders and ages. The user property information which is extracted from various experiments can be used as critical criteria in product design and evaluation, and it is necessary to develop the integrated database of cognition and emotion where to store the user property information. In this research, we design the user property metadata for supporting cognitive and emotional product design and then develop a prototype system. The metadata is designed to reflect the classification of cognition and emotion by investigating and classifying the previous studies related to sensing, cognition and emotion. The user property information is designed in RDF (Resource Description Framework), and a prototype system is developed to store user property information of cognition and emotion based on the designed metadata.

기술격차 감소로 인해 제품 차별화를 위한 인지 감성적 제품설계가 중요해지고 있다. 성별, 연령 등 다양한 사용자이 느끼는 감각(시각, 청각, 촉각 등), 인지(주의력, 기억력 등), 감성(심미성, 기능성 등)을 고려하기 위한 데이터베이스와 제품설계 가이드라인이 필요하다. 여러 실험으로부터 도출된 사용자 특성정보는 제품의 설계 및 평가 시 주요한 지표로 사용되고 있으며, 이를 저장할 통합적 인지 감성 데이터베이스를 구축하는 것이 필요하다. 본 연구에서는 인지 감성적 제품설계 지원을 위한 사용자 특성정보 메타데이터를 설계하고 프로토 타입 시스템으로 구축하였다. 감각, 인지, 감성에 관련된 기존 문헌들을 조사하고 분류하여 인지, 감성 유형별로 반영할 수 있도록 설계하였다. 제품설계/평가에 필요한 다양한 사용자 특성정보를 RDF 형식의 설계하였고, 설계된 메타데이터에 따라 인지, 감성에 관련된 사용자 특성정보를 저장할 수 있도록 프로토 타입 시스템을 구축하였다.

Keywords

References

  1. Apache Jena. https://jena.apache.org/.
  2. Arms, W. Y., Blanchi, C., and Overly, E. A., "An architecture for information in digital libraries," D-Lib Magazine, Vol. 3. No. 2, 1997.
  3. Barde, J., Libourel, T., and Maurel, P., "A metadata service for integrated management of knowledges related to coastal areas," Multimedia Tools and Applications, Vol. 25, No. 3, pp. 419-429, 2005. https://doi.org/10.1007/s11042-005-6544-5
  4. Brainmap, http://www.brainmap.org/.
  5. Ding, L., Finin, T., Joshi, A., Pan, R., Cost, R. S., Peng, Y., and Sachs, J., "Swoogle: a search and metadata engine for the semantic web," In Proceedings of the 13th ACM International Conference on Information and Knowledge Management, Washington D.C., 2004.
  6. Gangemi, A., Guarino, N., Masolo, C., Oltramari, A., and Schneider, L., "Sweetening ontologies with DOLCE," In International Conference on Knowledge Engineering and Knowledge Management, Berlin, 2002.
  7. Haynes, D., "Metadata for Information Management and Retrieval," Facet Publishing, 2004.
  8. Jacob, E. K., "Ontologies and the semantic web," Bulletin of the American Society for Information Science and Technology, Vol. 29, No. 4, pp. 19-22, 2003. https://doi.org/10.1002/bult.283
  9. Lee, M.-J., Lee, H.-J., Shim, J.-H. "Analysis and Modeling of Semantic Relationships in e-Catalog Domain", Journal of Society for e-Business Studies, Vol. 9, No. 3, pp. 243-258, 2004.
  10. Miguel Lopez, J., Gil Iranzo, R. M., Garcia Gonzalez, R., Cearreta, I., & Garay, N. (2008). Towards an ontology for describing emotions. Lecture Notes in Computer Science, Vol. 5288, pp. 96-104, 2008.
  11. Oh, S., Ahn, J. and Park, J., "Ontology Selection Ranking Model based on Semantic Similarity Approach," Journal of Society for e-Business Studies, Vol. 14, No. 2, pp. 95-116, 2009.
  12. Protege. http://protege.stanford.edu/.
  13. RDF. http://www.w3.org/TR/2014/RECrdf11-mt-2014022/.
  14. RDF Schema. http://www.w3.org/TR/rdf-schema/.
  15. SPARQL. http://www.w3.org/TR/rdf-sparql-query/.
  16. Staab, S., Erdmann, M., Maedche, A., and Decker, S., "An Extensible Approach for Modeling Ontologies in RDF(S)," ECDL 2000 Workshop on the Semantic Web, Lisbon, 2000.
  17. Turner, J. A. and Laird, A. R., "The cognitive paradigm ontology: design and application," Neuroinformatics, Vol. 10, No. 1, pp. 57-66, 2012. https://doi.org/10.1007/s12021-011-9126-x