Selective Temporal Knowledge Graph Reasoning
Zhongni Hou Xiaolong Jin Zixuan Li Long Bai Jiafeng Guo Xueqi Cheng
Temporal Knowledge Graph (TKG), which characterizes temporally evolving facts
in the form of (subject, relation, object, timestamp), has attracted much
attention recently. TKG reasoning aims to predict future facts based on given
historical ones. However, existing TKG reasoning models are unable to abstain
from predictions they are uncertain, which will inevitably bring risks in
real-world applications. Thus, in this paper, we propose an abstention
mechanism for TKG reasoning, which helps the existing models make selective,
instead of indiscriminate, predictions. Specifically, we develop a confidence
estimator, called Confidence Estimator with History (CEHis), to enable the
existing TKG reasoning models to first estimate their confidence in making
predictions, and then abstain from those with low confidence. To do so, CEHis
takes two kinds of information into consideration, namely, the certainty of the
current prediction and the accuracy of historical predictions. Experiments with
representative TKG reasoning models on two benchmark datasets demonstrate the
effectiveness of the proposed CEHis.