Temporal-Aware Heterogeneous Graph Reasoning with Multi-View Fusion for Temporal Question Answering
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912920123736064 |
|---|---|
| author | Wen, Wuzhenghong Zhou, Bowen Huang, Jinwen Wu, Xianjie Sun, Yuwei Pan, Su Li, Liang Liu, Jianting |
| author_facet | Wen, Wuzhenghong Zhou, Bowen Huang, Jinwen Wu, Xianjie Sun, Yuwei Pan, Su Li, Liang Liu, Jianting |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19569 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Temporal-Aware Heterogeneous Graph Reasoning with Multi-View Fusion for Temporal Question Answering Wen, Wuzhenghong Zhou, Bowen Huang, Jinwen Wu, Xianjie Sun, Yuwei Pan, Su Li, Liang Liu, Jianting Computation and Language Artificial Intelligence 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. |
| title | Temporal-Aware Heterogeneous Graph Reasoning with Multi-View Fusion for Temporal Question Answering |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2602.19569 |