Deep Active Alignment of Knowledge Graph Entities and Schemata
This addresses the challenge of integrating heterogeneous knowledge graphs for applications like data integration, with incremental improvements in alignment techniques.
The paper tackles the problem of aligning entities, relations, and classes across different knowledge graphs by proposing DAAKG, a method combining deep learning and active learning, which shows superior accuracy and generalization in experiments on benchmark datasets.
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.