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

arXiv: https://arxiv.org/abs/2501.04343 · HTML: https://mlsanity.com/paper/2501.04343/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 8 Jan 2025.

## Builds on (most cited first, 5 of 5)

- [A Dataset for Answering Time-Sensitive Questions](https://mlsanity.com/paper/2108.06314/) (2021)
- [Question Answering Over Temporal Knowledge Graphs](https://mlsanity.com/paper/2106.01515/) (2021)
- [Complex Temporal Question Answering on Knowledge Graphs](https://mlsanity.com/paper/2109.08935/) (2021)
- [Question Calibration and Multi-Hop Modeling for Temporal Question Answering](https://mlsanity.com/paper/2402.13188/) (2024)
- [Faithful Temporal Question Answering over Heterogeneous Sources](https://mlsanity.com/paper/2402.15400/) (2024)

## Built on by (most cited first, 5 of 5)

- [TRAVELER: A Benchmark for Evaluating Temporal Reasoning across Vague, Implicit and Explicit References](https://mlsanity.com/paper/2505.01325/) (2025)
- [Plan of Knowledge: Retrieval-Augmented Large Language Models for Temporal Knowledge Graph Question Answering](https://mlsanity.com/paper/2511.04072/) (2025)
- [Let the Agent Search: Autonomous Exploration Beats Rigid Workflows in Temporal Question Answering](https://mlsanity.com/paper/2603.01853/) (2026)
- [The Dynamic Gist-Based Memory Model (DGMM): A Memory-Centric Architecture for Artificial Intelligence](https://mlsanity.com/paper/2605.02106/) (2026)
- [ChronoQG: Towards a Temporally Expressive and Hop-Bounded Benchmark for Temporal Knowledge Graph Question Generation](https://mlsanity.com/paper/2607.14770/) (2026)

## Abstract (arXiv)

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: urlhttps://github.com/PascalSun/TimelineKGQA as an open source Python package.