Active Learning for Entity Alignment
This work addresses the challenge of efficient labeling for entity alignment in knowledge graphs, but it is incremental as it builds on existing active learning methods.
The paper tackles the problem of labeling entity alignments in knowledge graphs by proposing a framework with active and passive learning strategies, finding that passive learning achieves comparable performance to active learning.
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.