The ability to compare entities within a knowledge graph is a cornerstone technique for several applications, ranging from the integration of heterogeneous data to machine learning. It is of particular importance in the biomedical domain, where semantic similarity can be applied to the prediction of protein–protein interactions, associations between diseases and genes, cellular localization of proteins, among others. In recent years, several knowledge graph-based semantic similarity measures have been developed, but building a gold standard data set to support their evaluation is non-trivial.

中文翻译：

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生物医学领域基于知识图谱相似性的基准数据集集合

比较知识图中实体的能力是多种应用程序的基石技术，范围从异构数据的集成到机器学习。它在生物医学领域尤为重要，语义相似性可用于预测蛋白质-蛋白质相互作用、疾病与基因之间的关联、蛋白质的细胞定位等。近年来，已经开发了几种基于知识图谱的语义相似性度量，但是构建黄金标准数据集来支持它们的评估并非易事。