Knowledge graphs (KGs) store rich facts about the real world. In this paper, we study KG alignment, which aims to find alignment between not only entities but also relations and classes in different KGs. Alignment at the entity level can cross-fertilize alignment at the schema level. We propose a new KG alignment approach, called DAAKG, based on deep learning and active learning. With deep learning, it learns the embeddings of entities, relations and classes, and jointly aligns them in a semi-supervised manner. With active learning, it estimates how likely an entity, relation or class pair can be inferred, and selects the best batch for human labeling. We design two approximation algorithms for efficient solution to batch selection. Our experiments on benchmark datasets show the superior accuracy and generalization of DAAKG and validate the effectiveness of all its modules.

翻译：知识图谱（KGs）存储了关于现实世界的丰富事实。本文研究知识图谱对齐，旨在不仅对齐实体，还对齐不同知识图谱中的关系和类别。实体层面的对齐可以促进模式层面的对齐。我们提出了一种基于深度学习和主动学习的KG对齐新方法DAAKG。通过深度学习，该方法学习实体、关系和类别的嵌入，并以半监督方式联合对齐它们。通过主动学习，该方法估计实体、关系或类别对被推断的可能性，并选择最合适的一批样本进行人工标注。我们设计了两种近似算法来高效求解批次选择问题。在基准数据集上的实验表明，DAAKG具有卓越的准确性和泛化能力，并验证了其所有模块的有效性。