When Machine Learning Meets Knowledge Graph: A New Vision for Designing Network Intelligent Optimization Pipelines and Rules

With the continuous development of mobile communication networks, machine learning (ML) significantly saves on labor costs and enhances the efficiency of network operations and maintenance through automated decision-making and predictive analysis. However, ML-based network intelligent optimization is usually poorly interpretable and has difficulty capturing correlations in the face of vague network problems or complex data dimensions. In addition, the conclusion is not easily considered trustworthy without further validation, which cannot meet the reliability requirements of network operations and maintenance. This article provides a comprehensive review of knowledge graph (KG) applications in network intelligent optimization, proposing a novel KG-driven framework to enhance ML-based optimization pipelines. By leveraging KG to guide feature engineering, the proposed framework improves ML model performance and augments network optimization interpretability. Finally, the feasibility and effectiveness of the proposed framework are validated through a case study on KG-driven network coverage optimization, demonstrating its potential for advancing intelligent and trustworthy network management.