Using AI and NLP for Tacit Knowledge Conversion in Knowledge Management Systems: A Comparative Analysis

Abstract

Tacit knowledge, often implicit and embedded within individuals and organizational practices, plays a crucial role in knowledge management. Converting this tacit knowledge into explicit forms is vital for organizational effectiveness. This paper conducts a comparative analysis of NLP algorithms used for document and report mining in knowledge management systems (KMS) to facilitate the conversion of tacit knowledge. The focus is on algorithms that help extract tacit knowledge from documents and reports, as this knowledge is typically represented in semi-structured and document-based forms in natural language. NLP strategies, including text mining, information extraction, sentiment analysis, clustering, classification, recommendation systems, and affective computing, are explored for their effectiveness in this context. The paper provides a comprehensive analysis of the suitability of various NLP algorithms for tacit knowledge conversion within KMS, offering valuable insights for advancing research in this area.

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