Knowledge map creation and maintenance for virtual communities of practice

This paper proposes a knowledge map management system to facilitate knowledge management in virtual communities of practice. To realize the proposed knowledge map management, we develop knowledge map creation and maintenance functions by utilizing information retrieval and data mining techniques. The knowledge maps created respectively from the documents of the teachers' professional community, SCTNet, and the thesis repository at Taiwan's National Central Library, are evaluated by experts of these two domains. Knowledge maps generated by the system are accepted by domain experts from the evaluation since the degree of their modification of the automatically created knowledge maps is proportionally small. The knowledge structure representing the categories of community documents maintains its high purity, diversity, specificity, and structure adaptation by using the knowledge map maintenance function with limited computational cost. Thus, the knowledge map creation and maintenance mechanisms developed in this research enable the dynamic knowledge management of communities of practice on the Internet.

[1]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[2]  Hsinchun Chen,et al.  Document clustering for electronic meetings: an experimental comparison of two techniques , 1999, Decis. Support Syst..

[3]  Fu-Ren Lin,et al.  A Conceptual Model for Virtual Organizational Learning , 2001, J. Organ. Comput. Electron. Commer..

[4]  Gordon Pask,et al.  Learning Strategies and Individual Competence. , 1972 .

[5]  Hsiao-Tieh Pu,et al.  Important Issues on Chinese Information Retrieval , 1996, Int. J. Comput. Linguistics Chin. Lang. Process..

[6]  Gaston H. Gonnet,et al.  New Indices for Text: Pat Trees and Pat Arrays , 1992, Information Retrieval: Data Structures & Algorithms.

[7]  Gerald Salton,et al.  Automatic text processing , 1988 .

[8]  Lee-Feng Chien,et al.  PAT-tree-based keyword extraction for Chinese information retrieval , 1997, SIGIR '97.

[9]  Donald R. Morrison,et al.  PATRICIA—Practical Algorithm To Retrieve Information Coded in Alphanumeric , 1968, J. ACM.

[10]  Girish N. Punj,et al.  Cluster Analysis in Marketing Research: Review and Suggestions for Application , 1983 .

[11]  George Karypis,et al.  A Comparison of Document Clustering Techniques , 2000 .

[12]  C. A. Moore,et al.  FIELD‐DEPENDENT AND FIELD‐INDEPENDENT COGNITIVE STYLES AND THEIR EDUCATIONAL IMPLICATIONS , 1975 .

[13]  Chih-Ping Wei,et al.  Managing document categories in e-commerce environments: an evolution-based approach , 2002, Eur. J. Inf. Syst..

[14]  Dimitrios Gunopulos,et al.  Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.

[15]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[16]  Samuel Kaski,et al.  Self organization of a massive document collection , 2000, IEEE Trans. Neural Networks Learn. Syst..

[17]  Rocco Paolucci,et al.  The effects of cognitive style and knowledge structure on performance using a hypermedia learning system , 1998 .

[18]  Glenn J. Browne,et al.  Evoking Information in Probability Assessment: Knowledge Maps and Reasoning-Based Directed Questions , 1997 .

[19]  Donald E. Knuth,et al.  The art of computer programming: sorting and searching (volume 3) , 1973 .

[20]  Gwyneth Tseng,et al.  ACTS: an automatic Chinese text segmentation system for full text retrieval , 1995 .

[21]  Peter Willett,et al.  Inclusion of Relevance Information in the Term Discrimination Model , 1989, J. Documentation.

[22]  Roberto J. Bayardo,et al.  Athena: Mining-Based Interactive Management of Text Database , 2000, EDBT.

[23]  Chih-Ping Wei,et al.  A mining-based category evolution approach to managing online document categories , 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences.

[24]  Donald E. Knuth,et al.  The Art of Computer Programming: Volume 3: Sorting and Searching , 1998 .

[25]  Gaston H. Gonnet,et al.  Fast text searching for regular expressions or automaton searching on tries , 1996, JACM.

[26]  I. Nonaka A Dynamic Theory of Organizational Knowledge Creation , 1994 .

[27]  Henk Sol,et al.  Proceedings of the 54th Hawaii International Conference on System Sciences , 1997, HICSS 2015.

[28]  Andreas Rauber,et al.  Automatic labeling of self-organizing maps for information retrieval , 1999, ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378).

[29]  Ricardo Baeza-Yates,et al.  Information Retrieval: Data Structures and Algorithms , 1992 .

[30]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[31]  SaltonGerard,et al.  Term-weighting approaches in automatic text retrieval , 1988 .

[32]  Zimin Wu,et al.  Chinese Text Segmentation for Text Retrieval: Achievements and Problems , 1993, J. Am. Soc. Inf. Sci..

[33]  Gerard Salton,et al.  A vector space model for automatic indexing , 1975, CACM.

[34]  Donald E. Knuth,et al.  The art of computer programming, volume 3: (2nd ed.) sorting and searching , 1998 .

[35]  Chien Chou,et al.  The effect of navigation map types and cognitive styles on learners' performance in a computer-networked hypertext learning system , 1998 .