Abstract
In general, document classification research focuses on the automated placement of unseen documents into pre-defined categories. This is regarded as one core technical component of knowledge management systems, because it can support to handle explicit knowledge more systematically and improve knowledge sharing among the users. Document classification in knowledge management systems should support incremental knowledge acquisition and maintenance because of the dynamic knowledge changes involved. We propose the MCRDR document classifier as an incremental and maintainable document classification solution. Even though our system successfully supported personal level document classification, we did not examine its capability as a document classification tool in multi-user based knowledge management contexts. This paper focuses on the analysis of document classification results performed by multiple users. Our analysis reveals that even though the same documents and the classification structure are given to the users, they have very different document classification patterns and different acceptance results for each other’s classification results. Furthermore, our results show that the integration of multiple users’ classification may improve document classification performance in the knowledge management context.
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References
Polanyi, M.: The Tacit Dimension. Routledge & Kegan Paul, London (1996)
Marwick, A.D.: Knowledge Management Technology. IBM Systems Journal 40(4), 814–830 (2001)
Sebastiani, F.: Text Categorization in Text Mining and its Applications. In: Zanasi, A. (ed.) Text Mining and its Applications, A, pp. 109–129. WIT Press, Zanasi (2004)
Brucher, H., Knolmayer, G., Mittermayer, M.A.: Document Classification Methods for Organizing Explicit Knowledge. In: Proceedings of the Third European Conference on Organizational Knowledge, Learning, and Capabilities, Athens, Greece (2002)
Park, S.S., Kim, Y.S., Kang, B.H.: Web Document Classification: Managing Context Change. In: WWW/Internet 2004. Proceedings of the IADIS International Conference, Madrid, Spain, pp. 143–151 (2004)
Park, S.S., Kim, S.K., Kang, B.H.: Web Information Management System: Personalization and Generalization. In: WWW/Internet 2003. Proceedings of the IADIS International Conference, Algarve, Portugal, pp. 523–530 (2003)
Kim, Y.S., Park, S.S., Kang, B.H., Choi, Y.J.: Incremental Knowledge Management of Web Community Groups on Web Portals. In: Proceedings of the 5th International Conference on Practical Aspects of Knowledge Management. Vienna, Austria, pp. 198–207 (2004)
Compton, P., Edwards, G., Kang, B., Lazarus, L., Malor, R., Menzies, T., Preston, P., Srinivasan, A., Sammut, C.: Ripple down rules: possibilities and limitations. In: Proceedings of the 6th Banff AAAI Knowledge Acquisition for Knowledge Based Systems Workshop. Banff, Canada, pp. 6.1–6.20 (1991)
Compton, P., Jansen, R.: A Philosophical Basis for Knowledge Acquisition. Knowledge Acquisition 2(3), 241–258 (1990)
Kang, B., Compton, P., Preston, P.: Multiple Classification Ripple Down Rules: Evaluation and Possibilities. In: Proceedings of the 9th AAAI-Sponsored Banff Knowledge Acquisition for Knowledge-Based Systems Workshop. Banff, Canada, pp. 17.1–17.20 (1995)
Vazey, M., Richards, D.: Troubleshooting at the Call Centre: A Knowledge-based Approach. In: Proceedings of the Artificial Intelligence and Applications 2005, Innsbruck, Austria (2005)
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Kang, B.H., Kim, Y.S., Choi, Y.J. (2007). Does Multi-user Document Classification Really Help Knowledge Management?. In: Orgun, M.A., Thornton, J. (eds) AI 2007: Advances in Artificial Intelligence. AI 2007. Lecture Notes in Computer Science(), vol 4830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76928-6_34
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DOI: https://doi.org/10.1007/978-3-540-76928-6_34
Publisher Name: Springer, Berlin, Heidelberg
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