Knowledge Graph completion techniques offer a powerful framework for enriching Protein Protein Interaction (PPI) networks with missing or context specific links. This work presents a Knowledge Graph completion framework that integrates literature derived semantic information into PPI network modeling. Biological networks, particularly PPIs, are inherently dynamic. It changes with physiological conditions such as disease states, tissue types, or environmental stressors. Obtaining experimental measurements like gene expression data for every possible scenario is often impractical, especially in unforeseen situations like sudden pandemic outbreaks, underscoring the need for assigning context specific confidence scores to interactions. Using the STRING database as the foundational interaction network, we augment protein pairs with context specific confident scores. To generalize beyond explicitly annotated interactions, we train a Graph Convolutional Network (GCN) that treats edge prediction based on the similarity score to the context. This enables the model to predict novel, relevant interactions and refine network structures in a biologically meaningful manner. The proposed method provides a scalable approach for generating dynamic, literature-informed PPI networks, facilitating more accurate modeling of condition dependent molecular interactions.