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

• URL: http://arxiv.org/abs/2607.14770v1
• Date: Thu, 16 Jul 2026 09:53:26 GMT
• Title: ChronoQG: Towards a Temporally Expressive and Hop-Bounded Benchmark for Temporal Knowledge Graph Question Generation
• Abstract summary: Knowledge graph question generation (KGQG) aims to generate natural-language questions from structured graph evidence.<n>Existing KGQG benchmarks are mostly built on static knowledge graphs and do not encode the temporal scopes of graph facts.<n>We propose ChronoQG, the first temporally expressive and hop-bounded benchmark construction framework for TKGQG.
• Score: 9.596365499364323
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Evaluating List Construction and Temporal Understanding capabilities of Large Language Models [54.39278049092508]
  Large Language Models (LLMs) are susceptible to hallucinations and errors on particularly temporal understanding tasks.<n>We propose the Time referenced List based Question Answering (TLQA) benchmark that requires structured answers in list format aligned with corresponding time periods.<n>We investigate the temporal understanding and list construction capabilities of state-of-the-art generative models on TLQA in closed-book and open-domain settings.
  arXiv  Detail & Related papers  (2025-06-26T21:40:58Z)
• Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graphs for Retrieval-Augmented Generation [69.45495166424642]
  We develop a robust and discriminative QA benchmark to measure temporal, causal, and character consistency understanding in narrative documents.<n>We then introduce Entity-Event RAG (E2RAG), a dual-graph framework that keeps separate entity and event subgraphs linked by a bipartite mapping.<n>Across ChronoQA, our approach outperforms state-of-the-art unstructured and KG-based RAG baselines, with notable gains on causal and character consistency queries.
  arXiv  Detail & Related papers  (2025-06-06T10:07:21Z)
• TimeLogic: A Temporal Logic Benchmark for Video QA [64.32208175236323]
  We introduce the TimeLogic QA (TLQA) framework to automatically generate temporal logical questions.<n>We leverage 4 datasets, STAR, Breakfast, AGQA, and CrossTask, and generate 2k and 10k QA pairs for each category.<n>We assess the VideoQA model's temporal reasoning performance on 16 categories of temporal logic with varying temporal complexity.
  arXiv  Detail & Related papers  (2025-01-13T11:12:59Z)
• TimelineKGQA: A Comprehensive Question-Answer Pair Generator for Temporal Knowledge Graphs [11.496509633886161]
  We propose a novel categorization framework based on timeline-context relationships.<n>textbfTimelineKGQA is a universal temporal QA generator applicable to any TKGs.
  arXiv  Detail & Related papers  (2025-01-08T08:30:44Z)
• Self-Improvement Programming for Temporal Knowledge Graph Question Answering [31.33908040172437]
  Temporal Knowledge Graph Question Answering (TKGQA) aims to answer questions with temporal intent over Temporal Knowledge Graphs (TKGs) Existing end-to-end methods implicitly model the time constraints by learning time-aware embeddings of questions and candidate answers. We introduce a novel self-improvement Programming method for TKGQA (Prog-TQA)
  arXiv  Detail & Related papers  (2024-04-02T08:14:27Z)
• Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation [64.64849950642619]
  We develop an evaluation framework inspired by formal semantics for evaluating text-to-image models. We show that Davidsonian Scene Graph (DSG) produces atomic and unique questions organized in dependency graphs. We also present DSG-1k, an open-sourced evaluation benchmark that includes 1,060 prompts.
  arXiv  Detail & Related papers  (2023-10-27T16:20:10Z)
• Deep Temporal Graph Clustering [77.02070768950145]
  We propose a general framework for deep Temporal Graph Clustering (GC) GC introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal graphs. Our framework can effectively improve the performance of existing temporal graph learning methods.
  arXiv  Detail & Related papers  (2023-05-18T06:17:50Z)
• HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning [59.38797474903334]
  This paper proposes the textbfHistorical textbfStructure textbfMatching (textbfHiSMatch) model. It applies two structure encoders to capture the semantic information contained in the historical structures of the query and candidate entities. Experiments on six benchmark datasets demonstrate the significant improvement of the proposed HiSMatch model, with up to 5.6% performance improvement in MRR, compared to the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-10-18T09:39:26Z)
• Semantic Framework based Query Generation for Temporal Question Answering over Knowledge Graphs [19.851986862305623]
  We propose a temporal question answering method, SF-TQA, which generates query graphs by exploring the relevant facts of mentioned entities. Our evaluations show that SF-TQA significantly outperforms existing methods on two benchmarks over different knowledge graphs.
  arXiv  Detail & Related papers  (2022-10-10T08:40:28Z)
• TempoQR: Temporal Question Reasoning over Knowledge Graphs [11.054877399064804]
  This paper puts forth a comprehensive embedding-based framework for answering complex questions over Knowledge Graphs. Our method termed temporal question reasoning (TempoQR) exploits TKG embeddings to ground the question to the specific entities and time scope it refers to. Experiments show that TempoQR improves accuracy by 25--45 percentage points on complex temporal questions over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-12-10T23:59:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.