An Interactive Visual Paradigm for Knowledge Graph Question-Answering

Abstract

In an era of information overload, verifying data reliability and provenance is critical, yet knowledge graphs (KGs) often remain complex for non-expert users. This thesis introduces TRACE, a Reasoning and Answer-path Comprehension Engine, a visualization tool enhancing transparency in KG question answering (KGQA). By abstracting intricate KGs into intuitive meta-nodes, TRACE simplifies exploration of large, multi-topic datasets. Its interactive interface allows users to navigate semantic communities and trace reasoning paths, fostering trust through clear answer derivation. Unlike cluttered traditional graph visualizations, TRACE’s meta-node approach provides a scalable, user-friendly solution, concealing technical complexities while enabling robust query validation. Large language models support natural language query parsing and community summarization, making KGs accessible to diverse audiences. TRACE positions itself as a vital widget for information platforms, empowering users to counter misinformation confidently. A user study and pipeline evaluation confirmed TRACE’s intuitive interface excels for complex queries, though multi-hop paths pose challenges, while processing tests demonstrated its scalable paradigm for large datasets. By prioritizing transparency and usability, TRACE redefines KGs as reliable tools for knowledge discovery, laying a foundation for future systems to deliver trustworthy, accessible information in a digital landscape fraught with uncertainty.