In our research on Scholarly Analytics we are developing innovative approaches to generate value out of scholarly data by leveraging a number of different technologies, such as large-scale data mining, semantic technologies, machine learning, and visual analytics .

We are currently pursuing a number of research lines, including (but not limited to):

• New algorithms for the automatic generation of large-scale taxonomies of scientific knowledge.
• Intelligent tool support for the automatic annotation of scientific publications.
• Innovative visual analytics to help users to make sense of large-scale scholarly data.
• A prediction engine able to forecast the emergence of new research fields.
• A novel approach to modelling and forecasting the migration of ideas and technologies across research communities.

We collaborate with major publishers and universities to generate scalable applications, such as search engines, recommender systems, and analytics tools. In particular, we are currently working closely with Springer Nature in the development of a number of semantically-enhanced solutions, such as Smart Topic Miner, a web application that supports editors in classifying books with relevant metadata, and the Smart Book Recommender, a system that assists editors in deciding which products should be marketed at scientific venues.

More details about our research on Scholarly Analytics can be found at http://skm.kmi.open.ac.uk.