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

Named entities and WordNet words are important in defining the content of atext in which they occur. Named entities have ontological features, namely,their aliases, classes, and identifiers. WordNet words also have ontologicalfeatures, namely, their synonyms, hypernyms, hyponyms, and senses. Thosefeatures of concepts may be hidden from their textual appearance. Besides,there are related concepts that do not appear in a query, but can bring out themeaning of the query if they are added. The traditional constrained spreadingactivation algorithms use all relations of a node in the network that will addunsuitable information into the query. Meanwhile, we only use relationsrepresented in the query. We propose an ontology-based generalized Vector SpaceModel to semantic text search. It discovers relevant latent concepts in a queryby relation constrained spreading activation. Besides, to represent a wordhaving more than one possible direct sense, it combines the most specificcommon hypernym of the remaining undisambiguated multi-senses with the form ofthe word. Experiments on a benchmark dataset in terms of the MAP measure forthe retrieval performance show that our model is 41.9% and 29.3% better thanthe purely keyword-based model and the traditional constrained spreadingactivation model, respectively.

Discovering Latent Concepts and Exploiting Ontological Features for Semantic Text Search