Knowledge Nebula Crystallizer for Knowledge Liquidization & Crystallization – from a Theory to a Methodology of Knowledge Management -

We propose a new concept of knowledge management “Knowledge Liquidization and Crystallization”, and a system “Knowledge Nebula Crystallizer” to support the process. Though there are several theories about knowledge creation (Nonaka et al. 1995)(Fischer et al. 2001)(Shneiderman 1998) and a number of companies have realized their importance, most of them face to difficulty of applying them to their real workflows. It is because they have not mentioned how companies should apply them to their workflows. Though traditional knowledge management methods have tried to capture and accumulate “knowledge” itself, it is impossible because knowledge is not something clear-shaped but is embedded in a “context” where a person interacts with an artefact. What can be captured is data or information that describes knowledge and can be used to generate new knowledge (Hackbarth and Grover 1999). In this paper, we are going to describe our approach to a new methodology for knowledge management. As exhibition designing is an example of a task that highly depends on implicit knowledge of professionals and that requires a methodology of knowledge management, it was chosen as an application of our concept to a real work place. A methodology for knowledge acquisition in the domain and “Knowledge Nebula Crystallizer for Exhibition Designing” are described.

[1]  Jonathan Ostwald,et al.  Organic Perspectives of Knowledge Management , 2003 .

[2]  Ben Shneiderman,et al.  Codex, memex, genex: the pursuit of transformational technologies , 1998, Int. J. Hum. Comput. Interact..

[3]  K. A. Ericsson,et al.  Protocol Analysis: Verbal Reports as Data , 1984 .

[4]  野中 郁次郎,et al.  The Knowledge-Creating Company: How , 1995 .

[5]  Frank M. Shipman,et al.  Incremental formalization with the hyper-object substrate , 1999, TOIS.

[6]  Gerhard Fischer,et al.  Knowledge Management: Problems, Promises, Realities, and Challenges , 2001, IEEE Intell. Syst..

[7]  Adam Farquhar,et al.  The Road Ahead for Knowledge Management: An AI Perspective , 2000, AI Mag..

[8]  Masaki Suwa,et al.  Macroscopic analysis of design processes based on a scheme for coding designers' cognitive actions , 1998 .

[9]  Christopher Williamson,et al.  Dynamic queries for information exploration: an implementation and evaluation , 1992, CHI.

[10]  R. J. Bogumil,et al.  The reflective practitioner: How professionals think in action , 1985, Proceedings of the IEEE.

[11]  William H. Bolen,et al.  Why We Buy the Science of Shopping , 2000 .

[12]  Varun Grover,et al.  The Knowledge Repository: Organizational Memory Information Systems , 1999, Inf. Syst. Manag..

[13]  R. Emerson,et al.  Writing Ethnographic Fieldnotes , 1995 .

[14]  Koichi Hori,et al.  A model to explain and predict the effect of human-computer interaction in the articulation process for concept formation , 1996 .

[15]  Donald A. Sch The reflective practitioner: how professionals think in action , 1983 .

[16]  Kazumitsu Matsuzawa,et al.  Viewpoint-Based Measurement of Semantic Similarity between Words , 1995, AISTATS.

[17]  Koichi Hori,et al.  Enhancing creativity through reorganising mental space concealed in a research notes stack , 1998, Knowl. Based Syst..

[18]  Koichi Hori,et al.  A System for Aiding Creative Concept Formation , 1994, IEEE Trans. Syst. Man Cybern. Syst..