Semantic Technologies in Knowledge Management

Purpose: The general objective of this study was to explore semantic technologies in knowledge management. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to semantic technologies in knowledge management. Preliminary empirical review revealed that these technologies hold significant potential for enhancing knowledge organization, retrieval, and utilization within organizations. While theoretical benefits were recognized, practical implementation faced challenges such as technical constraints, organizational resistance, and the need for alignment with organizational objectives. Despite these hurdles, the study emphasized the importance of overcoming barriers through comprehensive approaches involving technological solutions, organizational change management, and stakeholder engagement. Overall, the study highlighted the transformative potential of semantic technologies in knowledge management but underscored the necessity of addressing challenges to realize their full benefits. Unique Contribution to Theory, Practice and Policy: The Social Construction of Technology (SCOT) theory, Actor-Network Theory (ANT) and Resource-Based View (RBV) theory may be used to anchor future studies on semantic technologies. The study made several recommendations to enhance the adoption and utilization of semantic technologies. It suggested developing standardized ontologies, prioritizing user-centric design principles, and providing ongoing training to improve digital literacy among practitioners. Additionally, the study emphasized the importance of governance mechanisms for ensuring data quality, fostering collaborative networks for knowledge sharing, and embracing a culture of experimentation and adaptation. These recommendations aimed to address challenges such as interoperability, usability, and trustworthiness, ultimately enabling organizations to leverage semantic technologies more effectively in their knowledge management practices.