### Rewiring Knowledge Graphs with GNN Link Prediction

2012 marked a pivotal year for the worlds of deep learning and knowledge graphs, setting the stage for innovations that continue to influence their evolution. Among these, the emergence of Convolutional Neural Networks (CNNs) for image classification and the introduction of knowledge graphs by Google were particularly transformative, enhancing data management and paving new paths for semantic analysis.

Graph Neural Networks (GNNs) have since merged the depth of deep learning with the complexity of graph-structured data, enabling sophisticated analyses across various domains—from social networks to bioinformatics. This blend has facilitated advancements in tasks like node classification, link prediction, and overall graph analysis.

In our research, we delve into utilizing GNN Link Prediction to enrich knowledge graphs with textual data, capturing the intricate dynamics of entity relationships often overlooked by attribute-based models. This approach not only unveils latent connections but also refines knowledge graph structures for more nuanced analysis.

In a prior study 'Rewiring Knowledge Graphs by Graph Neural Network Link Predictions' , we focused on rewiring text-based knowledge graphs through the use of GNN link prediction models, specifically targeting semantic knowledge graphs built from text documents. We utilized GNN link prediction techniques to modify these graphs, revealing hidden connections between nodes.

In another study 'Uncovering Hidden Connections: Granular Relationship Analysis in Knowledge Graphs' (2024), we also applied GNN link prediction models to semantic knowledge graphs to uncover hidden relationships within a detailed vector space. We focused on identifying 'graph connectors' that expose deeper network structures and used graph triangle analysis to delve into complex interactions.

The Enron email corpus serves as our testbed, allowing us to explore the potential of text-enhanced knowledge graphs in revealing hidden patterns within organizational communication. By focusing on direct interactions and employing transformer models for text embedding, we lay the groundwork for a knowledge graph that more accurately represents the complexity of real-world relationships.

Our findings underscore the significant impact of incorporating textual data and GNN Link Prediction in knowledge graph analysis. This methodology offers a more comprehensive view of entities' interactions, fostering a deeper understanding of complex networks. As we continue to refine these approaches, the potential for uncovering novel insights in data-rich environments appears boundless, promising exciting avenues for future research.