  Multi-hop question answering (MQA) under knowledge editing (KE) has garnered
significant attention in the era of large language models. However, existing
models for MQA under KE exhibit poor performance when dealing with questions
containing explicit temporal contexts. To address this limitation, we propose a
novel framework, namely TEMPoral knowLEdge augmented Multi-hop Question
Answering (TEMPLE-MQA). Unlike previous methods, TEMPLE-MQA first constructs a
time-aware graph (TAG) to store edit knowledge in a structured manner. Then,
through our proposed inference path, structural retrieval, and joint reasoning
stages, TEMPLE-MQA effectively discerns temporal contexts within the question
query. Experiments on benchmark datasets demonstrate that TEMPLE-MQA
significantly outperforms baseline models. Additionally, we contribute a new
dataset, namely TKEMQA, which serves as the inaugural benchmark tailored
specifically for MQA with temporal scopes.
