Agentic Knowledge-Graph Question Generation with Verifiable Evidence Paths
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Abstract
The rapid expansion of structured knowledge bases has catalyzed significant interest in automated question generation systems designed to facilitate natural language interactions with complex data. Despite substantial advancements in neural generation models, current methodologies fundamentally lack the capacity to provide transparent and verifiable reasoning paths, often resulting in outputs that suffer from hallucination and logical disconnects. This paper introduces a novel framework for agentic knowledge-graph question generation that intrinsically mandates the construction of verifiable evidence paths. By conceptualizing the generation process as an autonomous agent navigating a highly structured graph environment, our approach ensures that every generated question is inextricably linked to a specific, logically sound sequence of factual triples. The agent operates through a sequential decision-making paradigm, dynamically selecting optimal reasoning paths before conditioning the natural language generation module on the extracted subgraphs. Comprehensive experiments conducted across multiple benchmark datasets demonstrate that our proposed framework not only achieves state-of-the-art performance in standard linguistic quality metrics such as fluency and relevance but also establishes a new paradigm in output verifiability. Human evaluation further corroborates the superiority of the agentic approach in producing reliable, contextually grounded, and logically transparent questions.
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