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

Completion through the embedding representation of the knowledge graph (KGE)has been a research hotspot in recent years. Realistic knowledge graphs aremostly related to time, while most of the existing KGE algorithms ignore thetime information. A few existing methods directly or indirectly encode the timeinformation, ignoring the balance of timestamp distribution, which greatlylimits the performance of temporal knowledge graph completion (KGC). In thispaper, a temporal KGC method is proposed based on the direct encoding timeinformation framework, and a given time slice is treated as the finestgranularity for balanced timestamp distribution. A large number of experimentson temporal knowledge graph datasets extracted from the real world demonstratethe effectiveness of our method.

A Temporal Knowledge Graph Completion Method Based on Balanced Timestamp Distribution