论文标题
Autoweird：通过随机搜索确定的怪异翻译评分功能
AutoWeird: Weird Translational Scoring Function Identified by Random Search
论文作者
论文摘要
评分函数（SF）测量了知识图中三重态的合理性。不同的评分功能可以导致在不同知识图上的链接预测性能上造成巨大差异。在本报告中，我们描述了通过在开放图基准（OGB）上随机搜索发现的怪异评分函数。该评分函数（称为Autoweird）仅在三胞胎中使用尾部实体和关系来计算其合理性得分。实验结果表明，AutoweiD在OGBL-Wikikg2数据集上实现了TOP-1性能，但比OGBL-BIOKG数据集的其他方法的性能要差得多。通过分析这两个数据集的尾部实体分布和评估协议，我们将Autoweird在Ogbl-Wikikg2上的意外成功归因于不适当的评估和集中的尾巴实体分布。这样的结果可能会激发有关如何准确评估知识图的不同链接预测方法的性能的进一步研究。
Scoring function (SF) measures the plausibility of triplets in knowledge graphs. Different scoring functions can lead to huge differences in link prediction performances on different knowledge graphs. In this report, we describe a weird scoring function found by random search on the open graph benchmark (OGB). This scoring function, called AutoWeird, only uses tail entity and relation in a triplet to compute its plausibility score. Experimental results show that AutoWeird achieves top-1 performance on ogbl-wikikg2 data set, but has much worse performance than other methods on ogbl-biokg data set. By analyzing the tail entity distribution and evaluation protocol of these two data sets, we attribute the unexpected success of AutoWeird on ogbl-wikikg2 to inappropriate evaluation and concentrated tail entity distribution. Such results may motivate further research on how to accurately evaluate the performance of different link prediction methods for knowledge graphs.
