TimelineKGQA: A Comprehensive Question-Answer Pair Generator for Temporal Knowledge Graphs

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.

Recommended citation: Qiang Sun, Sirui Li, Du Huynh, Mark Reynolds, and Wei Liu. 2025. TimelineKGQA: A Comprehensive Question-Answer Pair Generator for Temporal Knowledge Graphs. In Companion Proceedings of the ACM on Web Conference 2025 (WWW 25). Association for Computing Machinery, New York, NY, USA, 797–800. https://doi.org/10.1145/3701716.3715308
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