论文标题
实体对齐的积极学习
Active Learning for Entity Alignment
论文作者
论文摘要
在这项工作中，我们为知识图数据集中实体对齐的标记提供了一个新颖的框架。选择人类标签的信息实例的不同策略建立了我们框架的核心。我们说明实体比对的标签与单个实例以及这些差异如何影响标签效率有何不同。根据这些考虑，我们提出并评估不同的主动和被动学习策略。我们的主要发现之一是被动学习方法可以有效地预先计算和更轻松地部署，实现与主动学习策略相当的绩效。
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.
