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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The objective of Entity Alignment (EA) is to identify equivalent entity pairsfrom multiple Knowledge Graphs (KGs) and create a more comprehensive andunified KG. The majority of EA methods have primarily focused on the structuralmodality of KGs, lacking exploration of multi-modal information. A fewmulti-modal EA methods have made good attempts in this field. Still, they havetwo shortcomings: (1) inconsistent and inefficient modality modeling thatdesigns complex and distinct models for each modality; (2) ineffective modalityfusion due to the heterogeneous nature of modalities in EA. To tackle thesechallenges, we propose PathFusion, consisting of two main components: (1) MSP,a unified modeling approach that simplifies the alignment process byconstructing paths connecting entities and modality nodes to represent multiplemodalities; (2) IRF, an iterative fusion method that effectively combinesinformation from different modalities using the path as an information carrier.Experimental results on real-world datasets demonstrate the superiority ofPathFusion over state-of-the-art methods, with 22.4%-28.9% absolute improvementon Hits@1, and 0.194-0.245 absolute improvement on MRR.

Universal Multi-modal Entity Alignment via Iteratively Fusing Modality Similarity Paths