Active Learning for Entity Alignment

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