Active Learning for Entity Alignment

• URL: http://arxiv.org/abs/2001.08943v3
• Date: Wed, 17 Mar 2021 15:10:00 GMT
• Title: Active Learning for Entity Alignment
• Authors: Max Berrendorf and Evgeniy Faerman and Volker Tresp
• Abstract summary: We show how the labeling of entity alignments is different from assigning class labels to single instances. 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.
• Score: 25.234850999782953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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