Building an Event Knowledge Graph Using Dynamic Resource Scheduling Optimization and Semantic Graph Convolutional Networks

The construction of Event Knowledge Graphs (EKGs) has emerged as a transformative approach for modeling relationships and extracting insights from dynamic and heterogeneous event data. However, the challenges of scalability, real-time processing, and semantic accuracy remain significant hurdles in EKG development. This work introduces an innovative framework that integrates Dynamic Resource Scheduling Optimization (DRSO) with Semantic Graph Convolutional Networks (SGCNs) to address these challenges. DRSO ensures efficient allocation of computational resources, enabling the real-time processing of large-scale event streams with minimal latency. Simultaneously, SGCNs enhance the semantic representation of event data by leveraging graph structures and contextual embeddings. The proposed method processes event streams dynamically, constructing a robust and semantically enriched EKG that captures intricate relationships among events. Experimental evaluations conducted on benchmark datasets demonstrate the effectiveness of the proposed framework. The results show significant improvements in processing speed, resource utilization, and semantic accuracy compared to existing state-of-the-art approaches. Additionally, the integration of DRSO and SGCNs enables scalable graph construction while preserving the semantic fidelity of event relationships. This research highlights the potential of combining dynamic resource management with advanced graph neural networks for real-time event processing and knowledge graph construction. The findings have implications for applications in areas such as anomaly detection, recommendation systems, and predictive analytics in dynamic environments.