提升时间问答的推理能力，融合时序信息与多视角知识
Temporal-Aware Heterogeneous Graph Reasoning with Multi-View Fusion for Temporal Question Answering
- 用时序感知的提问编码，结合语言模型与时间实体动态
- 通过时间感知的消息传递实现显式多跳推理
- 多视角注意力融合文本与图谱知识，适合时序问答任务
面向时间知识图谱的问答（TKGQA）因处理时间敏感查询而受到关注。现有方法仍存在三大问题：1）提问表示中对时间约束建模较弱，导致推理偏差；2）难以进行显式多跳推理；3）语言与图谱表示融合效果不佳。本文提出一种新框架，包含时序感知的提问编码、多跳图谱推理与多视角异构信息融合。具体包括：1）引入约束感知的提问表示，结合语言模型的语义线索与时间实体动态；2）设计时序感知的图神经网络，通过时间感知消息传递实现显式多跳推理；3）采用多视角注意力机制，更高效融合提问上下文与时间图谱知识。在多个TKGQA基准上的实验表明，该方法持续优于多种基线。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。