In this work, we propose a novel framework for the labeling of entity alignments in knowledge graph datasets. Different strategies to select informative instances for the human labeler build the core of our framework. We illustrate how the labeling of entity alignments is different from assigning class labels to single instances and how these differences affect the labeling efficiency. Based on these considerations we propose and evaluate different active and passive learning strategies. One of our main findings is that passive learning approaches, which can be efficiently precomputed and deployed more easily, achieve performance comparable to the active learning strategies.

翻译：在这项工作中,我们提议了一个在知识图表数据集中标出实体调整的新框架。为人类标签机构选择信息实例的不同战略构建了我们框架的核心。我们说明了实体调整的标签与将类标签划为单一实例的不同之处,以及这些差异如何影响标签效率。基于这些考虑,我们提出并评价不同的主动和被动学习战略。我们的主要发现之一是,被动学习方法(可以高效地预先计算和更容易地应用)能够实现与积极学习战略相似的业绩。