Developing an Enterprise Knowledge Identification Model Using Knowledge Map and Ant Colony Algorithm

Abstract

This research proposes an innovative approach for identifying enterprise knowledge assets through the integration of a knowledge map framework and the application of ant colony optimization algorithm. In today's knowledge-driven economy, organizations increasingly recognize the strategic importance of effectively managing and leveraging their knowledge assets to gain competitive advantage and drive innovation. However, identifying and extracting valuable knowledge from vast and diverse datasets poses significant challenges. To address this issue, this study introduces a novel model that combines the visual representation of knowledge maps with the optimization capabilities of ant colony algorithms to systematically identify and prioritize critical knowledge areas within enterprises. The knowledge map serves as a graphical representation of the organization's knowledge landscape, depicting the relationships and interdependencies among various knowledge domains, while the ant colony algorithm simulates the foraging behavior of ants to search for optimal paths and clusters of knowledge nodes. Through empirical validation and case studies, this research demonstrates the effectiveness and efficiency of the proposed model in identifying key knowledge assets, fostering collaboration, and facilitating decision-making processes within enterprises. By providing a systematic and data-driven approach to knowledge identification, this model offers practical implications for enhancing organizational learning, innovation, and performance in knowledge-intensive environments.