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 Graphs (KGs) store information in the form of (head, predicate,tail)-triples. To augment KGs with new knowledge, researchers proposed modelsfor KG Completion (KGC) tasks such as link prediction; i.e., answering (h; p;?) or (?; p; t) queries. Such models are usually evaluated with averagedmetrics on a held-out test set. While useful for tracking progress, averagedsingle-score metrics cannot reveal what exactly a model has learned -- orfailed to learn. To address this issue, we propose KGxBoard: an interactiveframework for performing fine-grained evaluation on meaningful subsets of thedata, each of which tests individual and interpretable capabilities of a KGCmodel. In our experiments, we highlight the findings that we discovered withthe use of KGxBoard, which would have been impossible to detect with standardaveraged single-score metrics.

KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models