A Framework for Knowledge Representation Integrated with Dynamic Network Analysis

Understanding the information residing in any system is of crucial importance. Knowledge Graphs are a tool for achieving such kinds of goals, as they hold the semantic interaction across the entities and, using links, connect them in a better representable way. In this paper, we proposed a dynamic network analysis framework for understanding the evolution of Knowledge Graphs across timelines. To validate our findings, we applied a thorough analysis of the movie recommendation Knowledge Graph, where we considered different snapshots of it. For example, past (historical information), present (current snapshot), and future (predictions based on historical data) information. For the predictions, we employ Graph Neural Network (GNN) modeling. We also compared our recommendation model with the latest related studies and achieved considerable results.