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

Abstract

Tacit knowledge, often implicit and deeply embedded within individuals and organizational practices, is critical for fostering innovation and decision-making in knowledge management systems (KMS). Converting tacit knowledge into explicit forms enhances organizational effectiveness by making this knowledge accessible and reusable. This paper presents a comparative analysis of natural language processing (NLP) algorithms used for document and report mining to facilitate tacit knowledge conversion. This study focuses on algorithms that extract insights from semi-structured and document-based natural language representations, commonly found in organizational knowledge artifacts. Key NLP strategies, including text mining, information extraction, sentiment analysis, clustering, classification, recommendation systems, and affective computing, are evaluated for their effectiveness in identifying and externalizing tacit knowledge. The findings highlight the relative strengths and limitations of these techniques, offering practical guidance for selecting suitable algorithms based on organizational needs. Additionally, this paper identifies challenges and emerging opportunities for advancing NLP-driven tacit knowledge conversion, providing actionable insights for researchers and practitioners aiming to enhance KMS capabilities.