Abstract
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 TimelineKGQA, a universal temporal QA generator applicable to any TKGs. The code is available at: https://github.com/PascalSun/TimelineKGQA as an open source Python package.