Knowledge graph embedding (KGE) models are often used to predict missing links for knowledge graphs (KGs). However, multiple KG embeddings can perform almost equally well for link prediction yet suggest conflicting predictions for certain queries, termed \textit{predictive multiplicity} in literature. This behavior poses substantial risks for KGE-based applications in high-stake domains but has been overlooked in KGE research. In this paper, we define predictive multiplicity in link prediction. We introduce evaluation metrics and measure predictive multiplicity for representative KGE methods on commonly used benchmark datasets. Our empirical study reveals significant predictive multiplicity in link prediction, with $8\%$ to $39\%$ testing queries exhibiting conflicting predictions. To address this issue, we propose leveraging voting methods from social choice theory, significantly mitigating conflicts by $66\%$ to $78\%$ according to our experiments.

翻译：知识图谱嵌入模型常用于预测知识图谱中的缺失链接。然而，多个知识图谱嵌入模型可能在链接预测任务上表现相近，却对某些查询给出相互矛盾的预测，这在文献中被称为\textit{预测多重性}。这种行为对基于知识图谱嵌入的高风险领域应用构成了重大风险，但在知识图谱嵌入研究中一直被忽视。本文定义了链接预测中的预测多重性。我们引入了评估指标，并在常用基准数据集上对代表性知识图谱嵌入方法进行了预测多重性度量。我们的实证研究表明，链接预测中存在显著的预测多重性，有$8\%$至$39\%$的测试查询表现出矛盾预测。为解决此问题，我们提出利用社会选择理论中的投票方法，根据我们的实验，该方法能将冲突显著降低$66\%$至$78\%$。