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

The identification of non-taxonomic relationships is a task that is carried out with learning and the creation of ontologies. Also, the manual construction of ontologies for experts and knowledge engineers is a costly and slow task, which is why it is necessary to create automatic or semi-automatic algorithms that speed up the procedure. In this research we propose an algorithm for the extraction of non-taxonomic relationships in an ontology of Artificial Intelligence (AI), evaluated through a data mining technique: association rules, which has statistical measures that determine the probability of occurrence between the concepts and the related connector verb. The experimental results indicate that 72 % of the relationships obtained in the algorithm exist in the ontology of AI.

Algoritmo basado en reglas de asociación para la extracción de relaciones no taxonómicas en corpus de dominio