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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper presents the STALKER knowledge base refinementsystem. Like its predecessor KRUST, STALKER proposes many alternativerefinements to correct the classification of each wrongly classifiedexample in the training set. However, there are two principaldifferences between KRUST and STALKER. Firstly, the range ofmisclassified examples handled by KRUST has been augmented by theintroduction of inductive refinement operators. Secondly,STALKER‘s testing phase has been greatly speeded up by using a TruthMaintenance System (TMS). The resulting system is moreeffective than other refinement systems because it generates manyalternative refinements. At the same time, STALKER is very efficientsince KRUST‘s computationally expensive implementation and testing ofrefined knowledge bases has been replaced by a TMS-based simulator.

Effective and Efficient Knowledge Base Refinement