GVR: A Recommendation Tool for Knowledge Graph Visualizations

Abstract

Knowledge graphs, which represent complex data through nodes and edges, offer immense potential for analysis but pose challenges in understanding desired outcomes. Visualizations serve as a crucial tool that enhances acces- sibility to knowledge graphs, yet their design demands expertise in data un- derstanding and visualization design. Recommendation systems for knowl- edge graph visualizations aim to lower the barrier for non-expert users in the process of information discovery by autonomously recommending and constructing (graph) visualizations from data. We introduce GVR (Graph Visualization Recommender), a system that aims to bridge the gap between raw knowledge graph data and informative visual representations, facilitat- ing efficient analysis and decision-making while paving the way for future advancements in this emerging field. The effectiveness of our approach was evaluated using generated sample data, highlighting its potential to recom- mend appropriate visualizations based on user interactions.