Enhancing NLP through GNN-Driven Knowledge Graph Rewiring and Document Classification

This study explores the application of Graph Neural Networks (GNNs) in Natural Language Processing (NLP), with a specific focus on Knowledge Graph Rewiring and Document Classification. Leveraging the distinct capabilities of GNNs, we aim to advance text analysis by revealing hidden semantic connections and improving recommendation systems. Our methodology introduces a novel approach for constructing and analyzing semantic graphs, employing GNN-driven techniques to uncover complex patterns and relationships within text data, often missed by traditional methods.We conduct a comparative analysis of GNN models to detect and classify intricate relationships in knowledge graphs derived from biographies of modern art artists. This research underscores GNNs' potential to not only enhance the accuracy and depth of classification tasks but also to provide a deeper understanding of text construction and interpretation. We critically examine the effectiveness of GNNs in managing noise and identifying outliers, highlighting the need for continued advancements in model refinement.Our findings demonstrate GNNs' ability to significantly improve data analysis through knowledge graph rewiring and document classification. Emerging as potent tools for delivering nuanced, context-aware insights, GNNs represent a major progression in NLP and beyond. By pioneering in knowledge representation and revealing deep semantic connections, this research paves the way for the broader application of GNNs in fields requiring detailed text analysis and sophisticated knowledge graph interpretation.