Research on Enhanced Graph Neural Network for Automated Knowledge Graph Construction

The construction of automated knowledge graphs has become a significant undertaking in the realms of artificial intelligence and natural language processing. It offers a structured representation of knowledge, which can be leveraged for a multitude of downstream applications. However, conventional methodologies frequently prove inadequate when attempting to discern intricate relationships and to process data that is sparse or of poor quality. To address these challenges, we present a novel framework based on enhanced Graph Neural Networks (GNNs) to enhance the precision and efficiency of knowledge graph construction. This method introduces a multi-layer attention mechanism to dynamically measure the relative importance of nodes and edges, and a hybrid loss function is designed to achieve a balance between entity recognition and relationship extraction. Furthermore, a data augmentation strategy is employed to address the issue of data sparsity, thereby enhancing the model's resilience. Extensive experimental analysis was conducted on multiple benchmark datasets, and the results demonstrate that the framework outperforms current state-of-the-art models in terms of accuracy and recall. This verifies the effectiveness and potential of the framework in practical applications.