How AI Linking Technology Works to Meet User Expectations
Session Abstract
Connecting users to full-text articles used to be a straightforward process of creating an OpenURL query that linked users from A&I resources to publisher sites. Today, it's not so simple. Several factors now need to be considered in creating a full-text link and deciding which one is used, including which publisher and aggregator sources have the article, the formats available to the user (PDF or HTML), whether the article is Open Access, if the version of record and alternative versions are available, if an article has been retracted or carries an expression of concern, and if the article is from a predatory journal. This presentation describes how the LibKey linking technology uses article-level intelligence and AI-based source selection to navigate the new terrain of linking to provide the fastest, most reliable, and most informed link to full text, every time, wherever the user starts their research journey.
Learning Outcome #1
At the end of this session, participants will be able to explain unique aspects of AI linking technology.
Learning Outcome #2
At the end of this session, participants will be able to explain the challenges posed in linking from hybrid open access journals.
Track
Discovery
Applicable Ex Libris Product
Leganto
Primo
Primo VE
SFX
Summon/360 Services
Target Audience Skill Level
None — General Audience