English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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 entitiesbut also relations and classes in different KGs. Alignment at the entity levelcan cross-fertilize alignment at the schema level. We propose a new KGalignment approach, called DAAKG, based on deep learning and active learning.With deep learning, it learns the embeddings of entities, relations andclasses, and jointly aligns them in a semi-supervised manner. With activelearning, it estimates how likely an entity, relation or class pair can beinferred, and selects the best batch for human labeling. We design twoapproximation algorithms for efficient solution to batch selection. Ourexperiments on benchmark datasets show the superior accuracy and generalizationof DAAKG and validate the effectiveness of all its modules.

Deep Active Alignment of Knowledge Graph Entities and Schemata