Karger partners with UNSILO on machine learning

UNSILO and Karger Publishers have agreed to create a number of artificial intelligence-based content enrichment solutions for its  range of digital biomedical content services. The new solutions will be based on UNSILO’s concept extraction engine, which uses machine learning to identify core concepts in scientific book chapters or journal articles.

The first solution to be launched eases the creation of article packages. Providing a degree of automation to a labor-intensive process, the solution will support the content selection for thematic collections such as the Karger 'Topic Article Packages' and the topic-focused German-language journals for medical practitioners, 'Karger Kompass', as well as other projects. Thanks to the UNSILO Package Manager, a SaaS-based content- management tool powered by flexible machine-learning algorithms, the automatic selection can be finely tuned to vary the degree of automation of the process.

Gabriella Karger, CEO of Karger Publishers, commented: 'Since my great-grandfather started the company in 1890 we have always been looking for innovations and cutting-edge technology to better keep up with the biomedical community's changing information needs. The partnership with UNSILO helps us to do so with new tools to further hone our content management. This, in turn, enables our worldwide client base in clinical and basic research to identify the exact scientific information they are looking for.'

Thomas Laursen, chief executive officer at UNSILO, added: 'We are very excited to be working with one of the most prestigious names in science and medical publishing. We have been impressed by the enthusiasm and willingness to explore new solutions that the Karger team has demonstrated.

'Working with Karger Publishers is particularly interesting because Karger hosts and manages its content directly, giving the publishing house exceptional control over it. We see the integration of UNSILO concept extraction with the Karger platform as an opportunity to fast-track some leading-edge innovation. We look forward to a long-lasting relationship making effective use of machine learning to aid both the researcher and the clinician.'