Former knowledge engineering research aimed at boosting automatic reasoning. However recent knowledge
management research focused on promoting the knowledge sharing and reusing among the people. Because
of the different aims between the two directions, former knowledge representation schemata, such as rule based
representation, frame from knowledge engineering research does not fit to the current knowledge management scenarios.
In this paper, for the purpose of building knowledge management systems for product design enterprises,
knowledge items are classified into seven types based on the semantics of their usage. Then their representations
are discussed respectively. Based on the above classification, a knowledge representation meta-model and a basic
domain ontology reference model for cooperative knowledge management systems are put forward. The reference
model is an abstraction that can be reused and extended in knowledge management systems of different enterprises.
Finally, the patterns of knowledge acquisition processes in cooperative knowledge management scenarios
of product design processes are studied.
LIU Xi-juan1* (刘溪涓), WANG Ying-lin2 (王英林)
. Semantic-Based Knowledge Categorization and Organization for Product Design Enterprises[J]. Journal of Shanghai Jiaotong University(Science), 2015
, 20(1)
: 106
-112
.
DOI: 10.1007/s12204-015-1596-9
[1] Bueno T C, Hoeschl H C, Bortolon A. Knowledge engineering suite: A tool to create ontologies for automatic knowledge representation in knowledgebased systems [C]//4th International Conference on Computer Science, Electronic Government. Berlin:Springer-Verlag, 2005: 249-260.
[2] Nonaka I. The knowledge creating company [J]. Harvard Business Review, 1991, 69(6): 96-104.
[3] Kebede G. Knowledge management: An information science perspective [J]. International Journal of Information Management, 2010, 30(5): 416-424.
[4] Kimble C. Knowledge management, codification and tacit knowledge [J]. Information Research, 2013, 18(2):561-577.
[5] Davenport T H, Prusak L. Working knowledge: How organization manage what they know [M].Boston, USA: Havard Business School Press, 1998:123-143.
[6] Ropohl G. Knowledge types in technology [J]. International Journal of Technology and Design Education,1997, 7(1-2): 65-72.
[7] Ramesh B, Tiwana A. Supporting collaborative process knowledge management in new product development teams [J]. Decision Support Systems, 1999, 27:213-235.
[8] Cormican K, O’Sullivan D. A collaborative knowledge management tool for product innovation management [J]. International Journal of Technology Management,2003, 26(1): 53-67.
[9] Lu Hui-min, Feng Bo-qin, Chen Xi. Extended topic map: Knowledge collaborative building for distributed knowledge resource [C]//Proceedings of the 2010 IEEE/IFIP Network Operations and Management Symposium. Osaka, Japan; IEEE, 2010: 128-135.
[10] Kim M P, Sang O, Joo W K. Tag based collaborative knowledge management system with crowdsourcing [J]. Journal of Internet Technology, 2013, 14 (5):859-866.
[11] Kondreddi S K, Triantafillou P, Weikum G.Combining information extraction and human computing for crowdsourced knowledge acquisition [C]//2014 IEEE International Conference on Data Engineering.Chicago, USA: IEEE, 2014: 988-999.
[12] Wright K. Personal knowledge management: Supporting individual knowledge worker performance [J].Knowledge Management Research and Practice, 2005,3: 156-165.
[13] Zhen Lu, Song Hai-tao, He Jun-tao. Recommender systems for personal knowledge management in collaborative environments [J]. Expert Systems with Applications,2012, 39: 12536-12542.
[14] Lee Changyong, Song Bomi, Park Yongtae. Design of convergent product concepts based on functionality:An association rule mining and decision tree approach [J]. Expert Systems with Applications, 2012, 39: 9534-9542.
[15] Bae J K, Kim J. Product development with data mining techniques: A case on design of digital camera [J]. Expert Systems with Applications, 2011, 38: 9274-9280.