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

During the early stages of developing Case-Based Reasoning (CBR) systems thedefinition of similarity measures is challenging since this task requirestransferring implicit knowledge of domain experts into knowledgerepresentations. While an entire CBR system is very explanatory, the similaritymeasure determines the ranking but do not necessarily show which featurescontribute to high (or low) rankings. In this paper we present our work onopening the knowledge engineering process for similarity modelling. This workpresent is a result of an interdisciplinary research collaboration between AIand public health researchers developing e-Health applications. During thiswork explainability and transparency of the development process is crucial toallow in-depth quality assurance of the by the domain experts.

On the Explanation of Similarity for Developing and Deploying CBR Systems