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 graph embedding (KGE) focuses on representing the entities andrelations of a knowledge graph (KG) into the continuous vector spaces, whichcan be employed to predict the missing triples to achieve knowledge graphcompletion (KGC). However, KGE models often only briefly learn structuralcorrelations of triple data and embeddings would be misled by the trivialpatterns and noisy links in real-world KGs. To address this issue, we build thenew paradigm of KGE in the context of causality and embedding disentanglement.We further propose a Causality-enhanced knowledge graph Embedding (CausE)framework. CausE employs causal intervention to estimate the causal effect ofthe confounder embeddings and design new training objectives to make stablepredictions. Experimental results demonstrate that CausE could outperform thebaseline models and achieve state-of-the-art KGC performance. We release ourcode in https://github.com/zjukg/CausE.

CausE: Towards Causal Knowledge Graph Embedding