Link Discovery plays a central role in the creation of knowledge bases that abide by the ﬁve Linked Data principles. Over the last years, several active learning approaches have been developed and used to facilitate the supervised learning of link speciﬁcations. Yet so far, these approaches have not taken the correlation between unlabeled examples into account when requiring labels from their user. In this paper, we address exactly this drawback by presenting the concept of the correlation-aware active learning of link speciﬁcations. We then present two generic approaches that implement this concept. The ﬁrst approach is based on graph clustering and can make use of intra-class correlation. The second relies on the activation-spreading paradigm and can make use of both intra- and inter-class correlations. We evaluate the accuracy of these approaches and compare them against a state-of-the-art link speciﬁcation
learning approach in ten diﬀerent settings. Our results show that our approaches outperform the state of the art by leading to speciﬁcations with higher F-scores.