Academic publisher, Cambridge University Press, and Denmark-based AI company, UNSILO, have 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.
Cambridge University Press built Cambridge Core, a new platform linking Cambridge books and journals, with the intention to bring market-leading and innovative tools to apply to their content. According to Nik Louch, Platform Engineering Director at Cambridge University Press, 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 their content, ranging from archaeology to neuroscience, and international law to Anglo-Saxon studies, the Press 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.
Brought to you by Scope e-Knowledge Center, a trusted global partner for digital content transformation solutions - Abstracting & Indexing (A&I), Knowledge Modeling (Taxonomies, Thesauri and Ontologies), and Metadata Enrichment & Entity Extraction.
