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

Heterogeneous information network (HIN) has shown its power of modeling realworld data as a multi-typed entity-relation graph. Meta-path is the keycontributor to this power since it enables inference by capturing theproximities between entities via rich semantic links. Previous HIN studies askusers to provide either 1) the meta-path(s) directly or 2) biased examples togenerate the meta-path(s). However, lots of HINs (e.g., YAGO2 and Freebase)have rich schema consisting of a sophisticated and large number of types ofentities and relations. It is impractical for users to provide the meta-path(s)to support the large scale inference, and biased examples will result inincorrect meta-path based inference, thus limit the power of the meta-path. Inthis paper, we propose a meta-path constrained inference framework to furtherrelease the ability of the meta-path, by efficiently learning the HIN inferencepatterns via a carefully designed tree structure; and performing unbiasedrandom walk inference with little user guidance. The experiment results onYAGO2 and DBLP datasets show the state-of-the-art performance of the meta-pathconstrained inference framework.

Meta-Path Constrained Random Walk Inference for Large-Scale Heterogeneous Information Networks