Context-KG: Context-Aware Knowledge Graph Visualization with User Preferences and Ontological Guidance

TL;DR

Context-KG introduces a novel, user-centered knowledge graph visualization framework that leverages ontological guidance and large language models to enhance interpretability and relevance in exploring complex data.

Contribution

It presents a new framework that integrates user preferences, ontological semantics, and LLMs to produce context-aware, interpretable KG visualizations tailored to individual queries.

Findings

Improved interpretability and relevance in KG visualization.

Enhanced user task performance demonstrated through user studies.

Semantic, ontology-guided layouts outperform traditional force-directed methods.

Abstract

Knowledge Graphs (KGs) are increasingly used to represent and explore complex, interconnected data across diverse domains. However, existing KG visualization systems remain limited because they fail to provide the context of user questions. They typically return only the direct query results and arrange them with force-directed layouts by treating the graph as purely topological. Such approaches overlook user preferences, ignore ontological distances and semantics, and provide no explanation for node placement. To address these challenges, we propose Context-KG, a context-aware KG visualization framework. Context-KG reframes KG visualization around ontology, context, and user intent. Using Large Language Models (LLMs), it iteratively extracts user preferences from natural language questions and context descriptions, identifying relevant node types, attributes, and contextual relations. These preferences drive a semantically interpretable, ontology-guided layout that is tailored to each query, producing type-aware regions. Context-KG also generates high-level insights unavailable in traditional methods, opening new avenues for effective KG exploration. Evaluations on real world KGs and a comprehensive user study demonstrate improved interpretability, relevance, and task performance, establishing Context-KG as a new paradigm for KG visualization.