Question answering over temporal knowledge graphs (KGs) efficiently uses
facts contained in a temporal KG, which records entity relations and when they
occur in time, to answer natural language questions (e.g., "Who was the
president of the US before Obama?"). These questions often involve three
time-related challenges that previous work fail to adequately address: 1)
questions often do not specify exact timestamps of interest (e.g., "Obama"
instead of 2000); 2) subtle lexical differences in time relations (e.g.,
"before" vs "after"); 3) off-the-shelf temporal KG embeddings that previous
work builds on ignore the temporal order of timestamps, which is crucial for
answering temporal-order related questions. In this paper, we propose a
time-sensitive question answering (TSQA) framework to tackle these problems.
TSQA features a timestamp estimation module to infer the unwritten timestamp
from the question. We also employ a time-sensitive KG encoder to inject
ordering information into the temporal KG embeddings that TSQA is based on.
With the help of techniques to reduce the search space for potential answers,
TSQA significantly outperforms the previous state of the art on a new benchmark
for question answering over temporal KGs, especially achieving a 32% (absolute)
error reduction on complex questions that require multiple steps of reasoning
over facts in the temporal KG.

本文提出了一种对时间知识图进行问答的框架，包括一个时间戳估计模块和一个考虑时间顺序的知识图嵌入编码器，能够有效地解决问题中时间相关的挑战，并且在时间知识图问答基准上获得了显著的性能提升。