  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.
