# ChronoQG: Towards a Temporally Expressive and Hop-Bounded Benchmark for Temporal Knowledge Graph Question Generation

arXiv: https://arxiv.org/abs/2607.14770 · HTML: https://mlsanity.com/paper/2607.14770/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 16 Jul 2026.

## Builds on (most cited first, 15 of 15)

- [Large-scale Simple Question Answering with Memory Networks](https://mlsanity.com/paper/1506.02075/) (2015)
- [Question Answering Over Temporal Knowledge Graphs](https://mlsanity.com/paper/2106.01515/) (2021)
- [An Interpretable Reasoning Network for Multi-Relation Question Answering](https://mlsanity.com/paper/1801.04726/) (2018)
- [EventKG: A Multilingual Event-Centric Temporal Knowledge Graph](https://mlsanity.com/paper/1804.04526/) (2018)
- [Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks](https://mlsanity.com/paper/1804.00823/) (2018)
- [Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation](https://mlsanity.com/paper/2310.08395/) (2023)
- [Knowledge Questions from Knowledge Graphs](https://mlsanity.com/paper/1610.09935/) (2016)
- [PAT-Questions: A Self-Updating Benchmark for Present-Anchored Temporal Question-Answering](https://mlsanity.com/paper/2402.11034/) (2024)
- [Enhancing Large Language Models with Pseudo- and Multisource- Knowledge Graphs for Open-ended Question Answering](https://mlsanity.com/paper/2402.09911/) (2024)
- [Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss](https://mlsanity.com/paper/1910.13108/) (2019)
- [Diversifying Question Generation over Knowledge Base via External Natural Questions](https://mlsanity.com/paper/2309.14362/) (2023)
- [SGSH: Stimulate Large Language Models with Skeleton Heuristics for Knowledge Base Question Generation](https://mlsanity.com/paper/2404.01923/) (2024)
- [TimelineKGQA: A Comprehensive Question-Answer Pair Generator for Temporal Knowledge Graphs](https://mlsanity.com/paper/2501.04343/) (2025)
- [EventKG+BT: Generation of Interactive Biography Timelines from a Knowledge Graph](https://mlsanity.com/paper/2012.06306/) (2020)
- [Historically Relevant Event Structuring for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2405.10621/) (2024)

## Abstract (arXiv)

Knowledge graph question generation (KGQG) aims to generate natural-language questions from structured graph evidence. Existing KGQG benchmarks, however, are mostly built on static knowledge graphs and do not encode the temporal scopes of graph facts. As a result, they cannot evaluate whether generated questions faithfully preserve temporal validity, event ordering, and answer-determining temporal constraints. In this paper, we study temporal knowledge graph question generation (TKGQG), where a generated question must be faithful to both the support subgraph and the temporal constraints required to identify the target answer. We propose ChronoQG, the first temporally expressive and hop-bounded benchmark construction framework for TKGQG. ChronoQG integrates a comprehensive temporal-constraint taxonomy, topology-temporal subgraph sampling, and trace-grounded question generation to construct temporally faithful questions. The framework produces four benchmark datasets from heterogeneous temporal knowledge graphs, totaling 16,011 verified questions. We evaluate representative LLM-based KGQG methods and prompting baselines across diverse TKGQG settings, including temporal-constraint counts, topological templates, and temporal-constraint types. The results show that existing methods struggle to preserve temporal constraints, especially under multi-constraint settings and harder temporal-constraint types. These findings reveal a clear gap between static KGQG and TKGQG, and establish ChronoQG as a challenging testbed for temporally faithful question generation.