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

2026-04-11

AI Summary

Existing knowledge graph visualization systems largely overlook user query context, preferences, and ontological semantics, relying solely on topological structure, which results in low relevance and poor interpretability of visual outcomes. This work proposes a novel visualization approach that integrates user preferences, contextual semantics, and ontological structure. It leverages large language models to iteratively extract user intent from natural language queries, driving a type-aware, zoned layout generation process. The method further incorporates ontology-guided semantic placement, context-aware node filtering, and force-directed optimization. Evaluated on real-world knowledge graphs and validated through user studies, the approach significantly enhances the relevance, interpretability, and task efficiency of visualizations, establishing a new paradigm for knowledge graph visualization.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Ontologies
• Machine Learning: Learning Preferences or Rankings

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Web data visualization

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.

Problem

• Knowledge Graph Visualization
• Context-Awareness
• User Preferences
• Ontological Guidance
• Semantic Layout

Innovation

• context-aware visualization
• ontology-guided layout
• Large Language Models (LLMs)
• knowledge graph exploration
• user preference modeling