Difference Views for Visual Graph Query Building

2025-08-07

AI Summary

Existing knowledge graph visualization tools inadequately support the dynamic evolution of user query intent during exploratory search. To address this, we propose an interactive visual query interface grounded in graph-difference computation: it incrementally computes and visualizes structural changes between successive SPARQL query graphs, simultaneously rendering shifts in query logic, natural-language requirement descriptions, and result distributions/instances—thereby enabling tripartite awareness of intent, structure, and outcome evolution. The system integrates natural-language interaction, graph-difference encoding, SPARQL generation, and multi-granularity result comparison, forming a cohesive end-to-end visual analytics pipeline. Case studies across diverse ontologies demonstrate that our approach significantly improves users’ exploration efficiency and semantic comprehension depth when navigating complex knowledge graphs.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Knowledge Representation and Reasoning: Ontologies
• Natural Language Processing: Question Answering

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Knowledge Graphs (KGs) contain vast amounts of linked resources that encode knowledge in various domains, which can be queried and searched for using specialized languages like SPARQL, a query language developed to query KGs. Existing visual query builders enable non-expert users to construct SPARQL queries and utilize the knowledge contained in these graphs. Query building is, however, an iterative and, often, visual process where the question of the user can change and differ throughout the process, especially for explorative search. Our visual querying interface communicates these change between iterative steps in the query building process using graph differences to contrast the changes and the evolution in the graph query. We also enable users to formulate their evolving information needs using a natural language interface directly integrated into the difference query view. We, furthermore, communicate the change in results in the result view by contrasting the differences in both result distribution and individual instances of the prototype graph and demonstrate the system's applicability through case studies on different ontologies and usage scenarios, illustrating how our system fosters, both, data exploration and analysis of domain-specific graphs.

Problem

• Enabling non-experts to visually build SPARQL queries for Knowledge Graphs
• Tracking and contrasting iterative changes in explorative graph query building
• Integrating natural language for evolving information needs in query views

Innovation

• Visual querying interface using graph differences
• Natural language interface for evolving queries
• Contrasting result differences in distribution and instances