Two-stage Generative Question Answering on Temporal Knowledge Graph
  Using Large Language Models
Yifu Gao Linbo Qiao Zhigang Kan Zhihua Wen Yongquan He Dongsheng Li
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.