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Cambridge University Press and UNSILO partner to deliver AI-based related links

Press Release: Cambridge University Press [September 16, 2019]

Cambridge, UK and Aarhus, Denmark, September 16, 2019. Cambridge University Press, one of the world's largest and most prestigious academic publishers, and UNSILO, Denmark-based AI company, today signed an agreement by which UNSILO will use machine-learning tools to identify related content across around one million journal articles and book chapters from the vast Cambridge corpus.

UNSILO applies machine learning and AI tools to identify significant concepts from a text corpus. These concepts form the basis of a wide range of solutions to publishing workflows, including building subject collections, identifying related articles, finding relevant journals, and many other areas. Uniquely, the UNSILO tools require no pre-existing taxonomy or ontology, and can be applied to multiple subject collections. Using a process of continuous ingestion, new articles and books are indexed and linked to other content on Cambridge Core within hours of publication.

Nik Louch, Platform Engineering Director at Cambridge University Press, commented:

When we designed and built Cambridge Core, the entirely new platform linking Cambridge books and journals, it was always our intention to bring market-leading and innovative tools to apply to our content. The UNSILO related articles link, delivered from cloud servers via a simple API, is a good example of how Core can be enhanced in this way. Because of the exceptionally wide subject range of our content, ranging from archaeology to neuroscience, and international law to Anglo-Saxon studies, we needed a solution that did not require the lengthy construction of multiple related ontologies and extensive manual tagging. Our trials in 2018 showed that users clearly appreciated and made use of the content links provided.

Thomas Laursen, CEO of UNSILO, commented:

Since UNSILO's foundation in 2014 we have worked with some of the largest names in publishing, but it is an honour to work with the longest-established academic publisher, Cambridge University Press, and its illustrious roster of authors. We are delighted we can be applying leading-edge machine-learning tools to such a rich content collection, and we look forward to delivering more tools via the Core platform in coming years.

About Cambridge University Press

Cambridge University Press ( ) dates from 1534 and is part of the University of Cambridge. Our mission is to unlock people's potential with the best learning and research solutions.

Our vision is a world of learning and research inspired by Cambridge, where:

  • We enable people to achieve success by providing the best learning and research solutions
  • We support our customers through continually improved content, experiences and care
  • We use our profit for purpose, contributing to society by furthering the mission of our University

Playing a leading role in today's global marketplace, we have over 50 offices around the globe, and we distribute our products to nearly every country in the world.


UNSILO is an artificial intelligence software company that develops advanced tools for text understanding and processing. UNSILO tools deliver dramatic workflow improvements by reducing processing time, while at the same time improving quality and accuracy. The UNSILO

Document Enrichment service forms the basis of over 20 separate functional solutions and APIs for publishers, including identifying trending topics as they emerge, automated collections, locating precise relatedness, and now manuscript evaluation, both pre- and post-submission. UNSILO works with world-leading information providers, including Springer Nature and Taylor and Francis, to improve internal publishing processes and external discoverability by authors and researchers. For more information, visit .

View Citation
Publication Year:2019
Type of Material:Press Release
Language English
Issue:September 16, 2019
Publisher:Cambridge University Press
Company: Cambridge University Press
Record Number:24605
Last Update:2023-11-21 21:26:52
Date Created:2019-09-24 14:05:39