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

Neural Information Retrieval (NIR) has significantly improved upon
heuristic-based IR systems. Yet, failures remain frequent, the models used
often being unable to retrieve documents relevant to the user's query. We
address this challenge by proposing a lightweight abstention mechanism tailored
for real-world constraints, with particular emphasis placed on the reranking
phase. We introduce a protocol for evaluating abstention strategies in a
black-box scenario, demonstrating their efficacy, and propose a simple yet
effective data-driven mechanism. We provide open-source code for experiment
replication and abstention implementation, fostering wider adoption and
application in diverse contexts.

神经信息检索 (NIR) 通过启发式的信息检索系统显著改进，但是仍然存在频繁的失败，通常所使用的模型无法检索与用户查询相关的文档。我们通过提出一个轻量级的针对现实约束的弃权机制，特别强调重新排名阶段，以解决这个挑战。我们在黑盒场景下介绍了一个评估弃权策略的协议，证明了其效果，并提出了一个简单而有效的数据驱动机制。我们提供了实验复现和弃权实现的开源代码，促进其在不同环境中的更广泛应用。