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This paper introduces ChronoQG, a novel benchmark for temporal knowledge graph question generation (TKGQG) that emphasizes the importance of temporal validity and event ordering in question generation from structured graph evidence. By integrating a comprehensive taxonomy of temporal constraints and employing topology-temporal subgraph sampling, the framework generates 16,011 verified questions across various temporal settings. Evaluation of existing LLM-based KGQG methods reveals significant challenges in maintaining temporal constraints, highlighting the need for advancements in this area.
Existing KGQG methods falter under temporal constraints, revealing a critical gap that ChronoQG aims to bridge with its innovative benchmark framework.
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.
