We are a research team focussing on Scholarly Knowledge, with expertise in Natural Language Processing, Semantic Web, Network Science, Knowledge Graphs and Scholarly Data.

Since 2014, we have been collaborating with Springer Nature on deploying new technologies. These include a tool for automatic classification of proceedings and other editorial products, a recommender systems for marketizing books, and a dashboard for exploring and comparing scientific venues.

More in detail, we are pursuing several research avenues:

• Exploring and making sense of scientific conferences and journals by means of the AIDA Dashboard.
• Automatic generation of large-scale taxonomies of scientific knowledge.
• Automatic annotation of scientific publications. This works includes both the CSO Classifier, an online service that supports the automatic classification of computer science papers with respect to the CSO Ontology, and the Smart Topic Miner, a tool in routine use at Springer Nature to automatically classify computer science proceedings.
• Innovative visual analytics to help users to make sense of large-scale scholarly data.
• Automatic forecasting of the emergence of new research fields.
• Modelling and forecasting the migration of ideas and technologies across research communities.

We released a number of knowledge graphs for exploring research dynamics, including:

• The Computer Science Ontology (CSO), the largest taxonomy of research topics in the field.
• The Academia/Industry DynAmics (AIDA) Knowledge Graph, an innovative resource describing 14M publications and 8M patents according to their topics and industrial sectors.
• The Computer Science Knowledge Graph (CS-KG), a large-scale automatically generated knowledge graph that describes 10M entities (e.g., tasks, methods, metrics, materials, others) relevant to the Computer Science according to 41M statements extracted from 6.7M research publications.

Please visit our website for our up-to-date list of publications: https://skm.kmi.open.ac.uk