Temporal-Aware Heterogeneous Graph Reasoning with Multi-View Fusion for Temporal Question Answering
arXiv:2602.19569v1 Announce Type: new Abstract: 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
arXiv:2602.19569v1 Announce Type: new Abstract: 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.
Executive Summary
The article proposes a novel framework for Temporal Question Answering over Temporal Knowledge Graphs, addressing existing limitations in incorporating temporal constraints, performing multi-hop reasoning, and fusing language and graph representations. The framework introduces a constraint-aware question representation, a temporal-aware graph neural network, and a multi-view attention mechanism, demonstrating consistent improvements over baselines on multiple benchmarks.
Key Points
- ▸ Temporal-aware question encoding to incorporate temporal constraints
- ▸ Multi-hop graph reasoning via time-aware message passing
- ▸ Multi-view heterogeneous information fusion for effective representation combination
Merits
Improved Temporal Reasoning
The framework's ability to incorporate temporal constraints and perform explicit multi-hop reasoning enhances its capacity for temporal question answering
Effective Representation Fusion
The multi-view attention mechanism allows for more effective combination of question context and temporal graph knowledge
Demerits
Complexity
The proposed framework may introduce additional computational complexity due to the incorporation of temporal-aware mechanisms and multi-view fusion
Expert Commentary
The article presents a significant advancement in temporal question answering, addressing key limitations in existing methods. The proposed framework's ability to incorporate temporal constraints, perform multi-hop reasoning, and fuse language and graph representations effectively makes it a valuable contribution to the field. However, further research is needed to explore the framework's scalability and applicability to diverse domains and datasets. Additionally, the complexity of the framework may require careful optimization to ensure efficient deployment in real-world applications.
Recommendations
- ✓ Future research should focus on optimizing the framework's computational complexity and exploring its applicability to diverse domains and datasets
- ✓ The development of more advanced temporal-aware mechanisms and multi-view fusion techniques is crucial for further enhancing the framework's performance and robustness
