Accepted extended abstract at DL4G@WWW 2020

Authors

Max Berrendorf, Evgeniy Faerman, Volker Tresp

Fifth International Workshop on Deep Learning for Graphs (DL4G@WWW2020), April 21, 2020, Taipei, Taiwan

Abstract

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. Moreover, we can dynamically learn to combine these scores to obtain an even better heuristic. more