A multi-facet taxonomy system with applications in unstructured knowledge management

Chi Fai Cheung, Wing Bun Lee, Y. Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

20 Citations (Scopus)

Abstract

Purpose - Unstructured knowledge management (UKM) becomes indispensable for the support of knowledge work. However, unstructured knowledge is inconvenient and difficult for sharing, organizing and acquisition. This paper seeks to present the development and implementation of a multi-facet taxonomy system (MTS) for effective management of unstructured knowledge. Design/methodology/approach - Multi-facet taxonomy is a multi-dimensional taxonomy which allows the classification of knowledge assets under multiple concepts at any levels of abstraction. The MTS system is based on five components: multi-dimensional taxonomy structure, thesaurus model, automatic classification mechanism, intelligent searching, and self-maintenance of taxonomy, respectively. Artificial intelligence (AI) and natural language process (NLP) technologies are used in the development of the MTS. Findings - With the successful development of the MTS, the accuracy of categorization of unstructured knowledge is significantly improved. It also allows an organization to capture the valuable tacit knowledge embedded in the unstructured knowledge assets. This helps an organization to explore business opportunities for continuous business improvement. Practical implications - The implementation of the MTS system not only dramatically reduces the human effort, time and cost for UKM but also allows an organization to capture valuable knowledge embedded in unstructured knowledge assets. Originality/value - As the knowledge work and task become more complex and are dynamically changing with time and involve multiple concepts, the MTS addresses the inadequacy of conventional single dimensional taxonomy for managing unstructured knowledge. The self-maintenance capability of the MTS ensures that the taxonomy is up-to-date and new knowledge is classified automatically for better knowledge sharing and acquisition.
Original languageEnglish
Pages (from-to)76-91
Number of pages16
JournalJournal of Knowledge Management
Volume9
Issue number6
DOIs
Publication statusPublished - 28 Nov 2005

Keywords

  • Artificial intelligence
  • Classification
  • Knowledge management

ASJC Scopus subject areas

  • Strategy and Management
  • Management of Technology and Innovation

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