Question answering over temporal knowledge graphs (TKGQA) has recently found
increasing interest. TKGQA requires temporal reasoning techniques to extract
the relevant information from temporal knowledge bases. The only existing TKGQA
dataset, i.e., CronQuestions, consists of temporal questions based on the facts
from a fixed time period, where a temporal knowledge graph (TKG) spanning the
same period can be fully used for answer inference, allowing the TKGQA models
to use even the future knowledge to answer the questions based on the past
facts. In real-world scenarios, however, it is also common that given the
knowledge until now, we wish the TKGQA systems to answer the questions asking
about the future. As humans constantly seek plans for the future, building
TKGQA systems for answering such forecasting questions is important.
Nevertheless, this has still been unexplored in previous research. In this
paper, we propose a novel task: forecasting question answering over temporal
knowledge graphs. We also propose a large-scale TKGQA benchmark dataset, i.e.,
ForecastTKGQuestions, for this task. It includes three types of questions,
i.e., entity prediction, yes-no, and fact reasoning questions. For every
forecasting question in our dataset, QA models can only have access to the TKG
information before the timestamp annotated in the given question for answer
inference. We find that the state-of-the-art TKGQA methods perform poorly on
forecasting questions, and they are unable to answer yes-no questions and fact
reasoning questions. To this end, we propose ForecastTKGQA, a TKGQA model that
employs a TKG forecasting module for future inference, to answer all three
types of questions. Experimental results show that ForecastTKGQA outperforms
recent TKGQA methods on the entity prediction questions, and it also shows
great effectiveness in answering the other two types of questions.

本文提出了一种新的任务：在时间知识图谱上进行预测问答，同时还提出了一个大规模 TKGQA 基准数据集 ForecastTKGQuestions，用于评估 QA 模型。最终提出 ForecastTKGQA 模型来回答三种类型的问题，实验结果表明它的表现优于最新的 TKGQA 方法。