链接预测还是预测失灵：知识图谱嵌入中不稳定的种子
Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings
AI总结：
本文系统分析了多种知识图谱嵌入模型在链接预测中的稳定性，发现高性能模型在三元组预测和嵌入空间上存在显著不稳定性，且随机种子、超参数等因素独立引发同等程度的不稳定，投票机制仅能有限提升稳定性。
摘要：
嵌入模型（KGEMs）是完成知识图谱的主要链接预测方法。标准评估协议强调基于排名的指标如MRR或Hits@$K$，但通常忽略随机种子对结果稳定性的影响。此外，这些指标掩盖了个别预测和嵌入空间组织中的潜在不稳定性。在这项工作中，我们对多个数据集上的多种KGEM进行了系统的稳定性分析。我们发现高性能模型实际上在三元组级别产生分歧预测，并具有高度可变的嵌入空间。通过隔离随机因素（即初始化、三元组排序、负采样、dropout、硬件），我们表明每个因素独立地引发相当程度的不稳定性。此外，对于给定模型，具有更好MRR的超参数配置并不能保证更稳定。而且，投票虽然是一种已知的补救机制，但只能提供有限的稳定性增强。这些发现凸显了当前基准测试协议的关键局限性，并引发了对KGEM用于知识图谱补全的可靠性的担忧。
英文摘要：
Embedding models (KGEMs) constitute the main link prediction approach to complete knowledge graphs. Standard evaluation protocols emphasize rank-based metrics such as MRR or Hits@$K$, but usually overlook the influence of random seeds on result stability. Moreover, these metrics conceal potential instabilities in individual predictions and in the organization of embedding spaces. In this work, we conduct a systematic stability analysis of multiple KGEMs across several datasets. We find that high-performance models actually produce divergent predictions at the triple level and highly variable embedding spaces. By isolating stochastic factors (i.e., initialization, triple ordering, negative sampling, dropout, hardware), we show that each independently induces instability of comparable magnitude. Furthermore, for a given model, hyperparameter configurations with better MRR are not guaranteed to be more stable. Moreover, voting, albeit a known remediation mechanism, only provides a limited enhancement of stability. These findings highlight critical limitations of current benchmarking protocols, and raise concerns about the reliability of KGEMs for knowledge graph completion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
