# Question Calibration and Multi-Hop Modeling for Temporal Question Answering

arXiv: https://arxiv.org/abs/2402.13188 · HTML: https://mlsanity.com/paper/2402.13188/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 48 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering](https://mlsanity.com/paper/2104.06378/) (2021)
- [GPT-GNN: Generative Pre-Training of Graph Neural Networks](https://mlsanity.com/paper/2006.15437/) (2020)
- [Knowledge Enhanced Contextual Word Representations](https://mlsanity.com/paper/1909.04164/) (2019)
- [Revisiting Graph Neural Networks: All We Have is Low-Pass Filters](https://mlsanity.com/paper/1905.09550/) (2019)
- [Variational Reasoning for Question Answering with Knowledge Graph](https://mlsanity.com/paper/1709.04071/) (2017)
- [Deep Bidirectional Language-Knowledge Graph Pretraining](https://mlsanity.com/paper/2210.09338/) (2022)
- [Learning Sequence Encoders for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/1809.03202/) (2018)
- [Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering](https://mlsanity.com/paper/2005.00646/) (2020)
- [Tensor Decompositions for temporal knowledge base completion](https://mlsanity.com/paper/2004.04926/) (2020)
- [Reinforcement Knowledge Graph Reasoning for Explainable Recommendation](https://mlsanity.com/paper/1906.05237/) (2019)
- [Entities as Experts: Sparse Memory Access with Entity Supervision](https://mlsanity.com/paper/2004.07202/) (2020)
- [Question Answering Over Temporal Knowledge Graphs](https://mlsanity.com/paper/2106.01515/) (2021)
- [Knowledge Graph Completion via Complex Tensor Factorization](https://mlsanity.com/paper/1702.06879/) (2017)
- [Reasoning about Entailment with Neural Attention](https://mlsanity.com/paper/1509.06664/) (2015)
- [Complex Temporal Question Answering on Knowledge Graphs](https://mlsanity.com/paper/2109.08935/) (2021)
- [TEQUILA: Temporal Question Answering over Knowledge Bases](https://mlsanity.com/paper/1908.03650/) (2019)
- [Time-aware Multiway Adaptive Fusion Network for Temporal Knowledge Graph Question Answering](https://mlsanity.com/paper/2302.12529/) (2023)
- [TempoQR: Temporal Question Reasoning over Knowledge Graphs](https://mlsanity.com/paper/2112.05785/) (2021)
- [Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs](https://mlsanity.com/paper/2203.00255/) (2022)
- [Dual Path Modeling for Semantic Matching by Perceiving Subtle Conflicts](https://mlsanity.com/paper/2302.12530/) (2023)

## Built on by (most cited first, 25 of 25)

- [SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models](https://mlsanity.com/paper/2601.03555/) (2026)
- [Not All Parameters Are Created Equal: Smart Isolation Boosts Fine-Tuning Performance](https://mlsanity.com/paper/2508.21741/) (2025)
- [LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2506.17966/) (2025)
- [Structural Reward Model: Enhancing Interpretability, Efficiency, and Scalability in Reward Modeling](https://mlsanity.com/paper/2509.25361/) (2025)
- [Temporal Knowledge Graph Question Answering: A Survey](https://mlsanity.com/paper/2406.14191/) (2024)
- [Comateformer: Combined Attention Transformer for Semantic Sentence Matching](https://mlsanity.com/paper/2412.07220/) (2024)
- [StructCoh: Structured Contrastive Learning for Context-Aware Text Semantic Matching](https://mlsanity.com/paper/2509.02033/) (2025)
- [Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models](https://mlsanity.com/paper/2604.10079/) (2026)
- [TimelineKGQA: A Comprehensive Question-Answer Pair Generator for Temporal Knowledge Graphs](https://mlsanity.com/paper/2501.04343/) (2025)
- [DPI: Exploiting Parameter Heterogeneity for Interference-Free Fine-Tuning](https://mlsanity.com/paper/2601.17777/) (2026)
- [Where Does Long-Context Supervision Actually Go? Effective-Context Exposure Balancing](https://mlsanity.com/paper/2605.10544/) (2026)
- [MemGuard: Persisting Verifier Signals for LLM-Agent Memory Governance](https://mlsanity.com/paper/2608.21867/) (2026)
- [Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning](https://mlsanity.com/paper/2604.14010/) (2026)
- [Using External knowledge to Enhanced PLM for Semantic Matching](https://mlsanity.com/paper/2505.06605/) (2025)
- [Boosting Neural Language Inference via Cascaded Interactive Reasoning](https://mlsanity.com/paper/2505.06607/) (2025)
- [HALO: Half Life-Based Outdated Fact Filtering in Temporal Knowledge Graphs](https://mlsanity.com/paper/2505.07509/) (2025)
- [Tag-Enriched Multi-Attention with Large Language Models for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2510.09224/) (2025)
- [Temporal-Aware Heterogeneous Graph Reasoning with Multi-View Fusion for Temporal Question Answering](https://mlsanity.com/paper/2602.19569/) (2026)
- [Efficient Task Adaptation in Large Language Models via Selective Parameter Optimization](https://mlsanity.com/paper/2604.17051/) (2026)
- [Do LLMs Capture Embodied Cognition and Cultural Variation? Cross-Linguistic Evidence from Demonstratives](https://mlsanity.com/paper/2604.25423/) (2026)
- [CRAFTQA: A Code-Driven Adaptive Framework for Complex Structured Data Reasoning](https://mlsanity.com/paper/2606.02170/) (2026)
- [Multi-Granularity Reasoning for Natural Language Inference](https://mlsanity.com/paper/2606.05181/) (2026)
- [Scope3Trace: Evidence-Based Identification and Extraction of Scope 3 GHG Emissions from Sustainability Reports](https://mlsanity.com/paper/2607.17122/) (2026)
- [AlignDiff: Exploiting Model-Intrinsic Information for Better Preference Data Selection](https://mlsanity.com/paper/2609.05899/) (2026)
- [Counterfactual Rollout Replay: Forkable Environments as Free Process Rewards for Software Engineering Agents](https://mlsanity.com/paper/2609.33875/) (2026)

## Abstract (arXiv)

Many models that leverage knowledge graphs (KGs) have recently demonstrated remarkable success in question answering (QA) tasks. In the real world, many facts contained in KGs are time-constrained thus temporal KGQA has received increasing attention. Despite the fruitful efforts of previous models in temporal KGQA, they still have several limitations. (I) They adopt pre-trained language models (PLMs) to obtain question representations, while PLMs tend to focus on entity information and ignore entity transfer caused by temporal constraints, and finally fail to learn specific temporal representations of entities. (II) They neither emphasize the graph structure between entities nor explicitly model the multi-hop relationship in the graph, which will make it difficult to solve complex multi-hop question answering. To alleviate this problem, we propose a novel Question Calibration and Multi-Hop Modeling (QC-MHM) approach. Specifically, We first calibrate the question representation by fusing the question and the time-constrained concepts in KG. Then, we construct the GNN layer to complete multi-hop message passing. Finally, the question representation is combined with the embedding output by the GNN to generate the final prediction. Empirical results verify that the proposed model achieves better performance than the state-of-the-art models in the benchmark dataset. Notably, the Hits@1 and Hits@10 results of QC-MHM on the CronQuestions dataset's complex questions are absolutely improved by 5.1% and 1.2% compared to the best-performing baseline. Moreover, QC-MHM can generate interpretable and trustworthy predictions.