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

Commonsense knowledge graph reasoning(CKGR) is the task of predicting amissing entity given one existing and the relation in a commonsense knowledgegraph (CKG). Existing methods can be classified into two categories generationmethod and selection method. Each method has its own advantage. Wetheoretically and empirically compare the two methods, finding the selectionmethod is more suitable than the generation method in CKGR. Given theobservation, we further combine the structure of neural Text Encoder andKnowledge Graph Embedding models to solve the selection method's two problems,achieving competitive results. We provide a basic framework and baseline modelfor subsequent CKGR tasks by selection methods.

Commonsense Knowledge Graph Reasoning by Selection or Generation? Why?