# Temporal-Aware Heterogeneous Graph Reasoning with Multi-View Fusion for Temporal Question Answering

arXiv: https://arxiv.org/abs/2602.19569 · HTML: https://mlsanity.com/paper/2602.19569/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 27 of its 36 references that are in the map.
- First posted on arXiv 23 Feb 2026.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 27 of 27)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering](https://mlsanity.com/paper/2104.06378/) (2021)
- [Learning Sequence Encoders for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/1809.03202/) (2018)
- [TableBench: A Comprehensive and Complex Benchmark for Table Question Answering](https://mlsanity.com/paper/2408.09174/) (2024)
- [Tensor Decompositions for temporal knowledge base completion](https://mlsanity.com/paper/2004.04926/) (2020)
- [Question Answering Over Temporal Knowledge Graphs](https://mlsanity.com/paper/2106.01515/) (2021)
- [TEQUILA: Temporal Question Answering over Knowledge Bases](https://mlsanity.com/paper/1908.03650/) (2019)
- [Contrastive Learning Reduces Hallucination in Conversations](https://mlsanity.com/paper/2212.10400/) (2022)
- [Towards Benchmarking and Improving the Temporal Reasoning Capability of Large Language Models](https://mlsanity.com/paper/2306.08952/) (2023)
- [Time-aware Multiway Adaptive Fusion Network for Temporal Knowledge Graph Question Answering](https://mlsanity.com/paper/2302.12529/) (2023)
- [Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs](https://mlsanity.com/paper/2203.00255/) (2022)
- [Question Calibration and Multi-Hop Modeling for Temporal Question Answering](https://mlsanity.com/paper/2402.13188/) (2024)
- [DABERT: Dual Attention Enhanced BERT for Semantic Matching](https://mlsanity.com/paper/2210.03454/) (2022)
- [Dual Path Modeling for Semantic Matching by Perceiving Subtle Conflicts](https://mlsanity.com/paper/2302.12530/) (2023)
- [Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion](https://mlsanity.com/paper/2210.08471/) (2022)
- [Searching for Optimal Subword Tokenization in Cross-domain NER](https://mlsanity.com/paper/2206.03352/) (2022)
- [Robust Lottery Tickets for Pre-trained Language Models](https://mlsanity.com/paper/2211.03013/) (2022)
- [Who Stole Your Data? A Method for Detecting Unauthorized RAG Theft](https://mlsanity.com/paper/2510.07728/) (2025)
- [Progressive Mastery: Customized Curriculum Learning with Guided Prompting for Mathematical Reasoning](https://mlsanity.com/paper/2506.04065/) (2025)
- [Not All Parameters Are Created Equal: Smart Isolation Boosts Fine-Tuning Performance](https://mlsanity.com/paper/2508.21741/) (2025)
- [Unleashing Potential of Evidence in Knowledge-Intensive Dialogue Generation](https://mlsanity.com/paper/2309.08380/) (2023)
- [Resolving Word Vagueness with Scenario-guided Adapter for Natural Language Inference](https://mlsanity.com/paper/2405.12434/) (2024)
- [Structural Reward Model: Enhancing Interpretability, Efficiency, and Scalability in Reward Modeling](https://mlsanity.com/paper/2509.25361/) (2025)
- [TwiRGCN: Temporally Weighted Graph Convolution for Question Answering over Temporal Knowledge Graphs](https://mlsanity.com/paper/2210.06281/) (2022)
- [HoPE: Hyperbolic Rotary Positional Encoding for Stable Long-Range Dependency Modeling in Large Language Models](https://mlsanity.com/paper/2509.05218/) (2025)
- [Comateformer: Combined Attention Transformer for Semantic Sentence Matching](https://mlsanity.com/paper/2412.07220/) (2024)
- [DeCoRL: Decoupling Reasoning Chains via Parallel Sub-Step Generation and Cascaded Reinforcement for Interpretable and Scalable RLHF](https://mlsanity.com/paper/2511.19097/) (2025)

## Abstract (arXiv)

Question Answering over Temporal Knowledge Graphs (TKGQA) has attracted growing interest for handling time-sensitive queries. However, existing methods still struggle with: 1) weak incorporation of temporal constraints in question representation, causing biased reasoning; 2) limited ability to perform explicit multi-hop reasoning; and 3) suboptimal fusion of language and graph representations. We propose a novel framework with temporal-aware question encoding, multi-hop graph reasoning, and multi-view heterogeneous information fusion. Specifically, our approach introduces: 1) a constraint-aware question representation that combines semantic cues from language models with temporal entity dynamics; 2) a temporal-aware graph neural network for explicit multi-hop reasoning via time-aware message passing; and 3) a multi-view attention mechanism for more effective fusion of question context and temporal graph knowledge. Experiments on multiple TKGQA benchmarks demonstrate consistent improvements over multiple baselines.