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

With the increasing number of published Web servicesproviding similar functionalities, it’s very tedious for aservice consumer to make decision to select the appropriate oneaccording to her/his needs. In this paper, we explore severalprobabilistic topic models: Probabilistic Latent Semantic Analysis(PLSA), Latent Dirichlet Allocation (LDA) and CorrelatedTopic Model (CTM) to extract latent factors from web servicedescriptions. In our approach, topic models are used as efficientdimension reduction techniques, which are able to capture semanticrelationships between word-topic and topic-service interpretedin terms of probability distributions. To address the limitation ofkeywords-based queries, we represent web service description asa vector space and we introduce a new approach for discoveringand ranking web services using latent factors. In our experiment,we evaluated our Service Discovery and Ranking approachby calculating the precision (P@n) and normalized discountedcumulative gain (NDCGn).

Correlated Topic Model for Web Services Ranking