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.

本文提出了一种针对时间知识图谱 (TKG) 推理的避让机制，通过开发一种称为历史置信度估计器 (CEHis) 的置信度估计器，使现有的 TKG 推理模型能够先估计其在进行预测时的置信度，然后避免那些置信度较低的预测。实验证明所提出的 CEHis 对于代表性的 TKG 推理模型在两个基准数据集上具有有效性。

选择性时间知识图谱推理

Selective Temporal Knowledge Graph Reasoning

This paper addresses the problem of selective classification for deep neural
networks, where a model is allowed to abstain from low-confidence predictions
to avoid potential errors. Specifically, we tackle the problem of optimizing
the confidence estimator of a fixed classifier, aiming to enhance its
misclassification detection performance, i.e., its ability to discriminate
between correct and incorrect predictions by assigning higher confidence values
to the correct ones. Previous work has found that different classifiers exhibit
varying levels of misclassification detection performance, particularly when
using the maximum softmax probability (MSP) as a measure of confidence.
However, we argue that these findings are mainly due to a sub-optimal
confidence estimator being used for each model. To overcome this issue, we
propose a simple and efficient post-hoc confidence estimator, named
$p$-NormSoftmax, which consists of transforming the logits through $p$-norm
normalization and temperature scaling, followed by taking the MSP, where $p$
and the temperature are optimized based on a hold-out set. This estimator can
be easily applied on top of an already trained model and, in many cases, can
significantly improve its selective classification performance. When applied to
84 pretrained Imagenet classifiers, our method yields an average improvement of
16% in the area under the risk-coverage curve (AURC), exceeding 40% for some
models. Furthermore, after applying $p$-NormSoftmax, we observe that these
models exhibit approximately the same level of misclassification detection
performance, implying that a model's selective classification performance is
almost entirely determined by its accuracy at full coverage.

本文介绍了一种简单高效的后验置信度估计器 - $p$-NormSoftmax，它可以大幅提高预训练模型的选择性分类性能，并在实验中证明了此方法的有效性。