Temporal knowledge graph question answering (TKGQA) poses a significant
challenge task, due to the temporal constraints hidden in questions and the
answers sought from dynamic structured knowledge. Although large language
models (LLMs) have made considerable progress in their reasoning ability over
structured data, their application to the TKGQA task is a relatively unexplored
area. This paper first proposes a novel generative temporal knowledge graph
question answering framework, GenTKGQA, which guides LLMs to answer temporal
questions through two phases: Subgraph Retrieval and Answer Generation. First,
we exploit LLM's intrinsic knowledge to mine temporal constraints and
structural links in the questions without extra training, thus narrowing down
the subgraph search space in both temporal and structural dimensions. Next, we
design virtual knowledge indicators to fuse the graph neural network signals of
the subgraph and the text representations of the LLM in a non-shallow way,
which helps the open-source LLM deeply understand the temporal order and
structural dependencies among the retrieved facts through instruction tuning.
Experimental results demonstrate that our model outperforms state-of-the-art
baselines, even achieving 100\% on the metrics for the simple question type.

提出一种新颖的生成式时间知识图问答框架（GenTKGQA），通过两个阶段引导 LLMs 回答时间性问题：子图检索和答案生成。在第一个阶段，利用 LLM 的内在知识来挖掘问题中的时间约束和结构链接，从而缩小子图搜索空间。在第二个阶段，设计虚拟知识指示器将子图的图神经网络信号与 LLM 的文本表示非浅层次地融合，帮助开源 LLM 深入理解检索到的事实中的时间顺序和结构依赖关系。实验结果表明，该模型优于最先进的基准方法，甚至对于简单问题类型的指标达到 100％。