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 give conflicting predictions for unseen queries. This phenomenon is termed \textit{predictive multiplicity} in the literature. It poses substantial risks for KGE-based applications in high-stake domains but has been overlooked in KGE research. We define predictive multiplicity in link prediction, 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. We address this issue by leveraging voting methods from social choice theory, significantly mitigating conflicts by $66\%$ to $78\%$ in our experiments.

翻译：知识图谱嵌入（KGE）模型常用于预测知识图谱（KG）中的缺失链接。然而，多个知识图谱嵌入模型在链接预测任务上可能表现近乎同等优异，却对未见查询给出相互冲突的预测。这一现象在文献中被称为\textit{预测多样性}。它给基于知识图谱嵌入的高风险领域应用带来了重大风险，但在知识图谱嵌入研究中一直被忽视。我们定义了链接预测中的预测多样性，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样性度量。我们的实证研究表明，链接预测中存在显著的预测多样性，有$8\%$至$39\%$的测试查询表现出冲突的预测。我们通过借鉴社会选择理论中的投票方法来解决这一问题，在我们的实验中，冲突显著减少了$66\%$至$78\%$。