TimelineKGQA

Question answering over temporal knowledge graphs (TKGs) is crucial for understanding evolving facts and relationships, yet its development is hindered by limited datasets and difficulties in generating custom QA pairs. We propose a novel categorization framework based on timeline-context relationships, along with \textbf{TimelineKGQA}, a universal temporal QA generator applicable to any TKGs. The code is available at: \url{https://github.com/PascalSun/TimelineKGQA} as an open source Python package.

We present a comprehensive temporal question categorization framework and a universal TKGQA dataset generator that addresses the limitations of existing datasets. Our framework introduces a systematic approach to classify question complexity through context facts, answer focus, and temporal operations, while identifying four key temporal capabilities (TCR, TPR, TSO, and TAO). Using our \textbf{TimelineKGQA} generator to create two benchmark datasets, we demonstrate that question difficulty aligns with our complexity categorization through empirical evaluation. This work provides a foundation for developing and evaluating more advanced TKGQA solutions, and enables widespread application in private domains.