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

Entity Alignment (EA) aims to find equivalent entity pairs between KGs, whichis the core step of bridging and integrating multi-source KGs. In this paper,we argue that existing GNN-based EA methods inherit the inborn defects fromtheir neural network lineage: weak scalability and poor interpretability.Inspired by recent studies, we reinvent the Label Propagation algorithm toeffectively run on KGs and propose a non-neural EA framework -- LightEA,consisting of three efficient components: (i) Random Orthogonal LabelGeneration, (ii) Three-view Label Propagation, and (iii) Sparse SinkhornIteration. According to the extensive experiments on public datasets, LightEAhas impressive scalability, robustness, and interpretability. With a mere tenthof time consumption, LightEA achieves comparable results to state-of-the-artmethods across all datasets and even surpasses them on many.

LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation