The SKM team aims to develop innovative approaches to generating value from scholarly data by leveraging large-scale data mining, semantic technologies, machine learning, and visual analytics. Francesco Osborne has been leading the team since 2014.

We are pursuing several research avenues, including:

• 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 work 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 routinely used 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 is an innovative resource that describes 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 25M entities (e.g., tasks, methods, metrics, materials, others) relevant to the Computer Science according extracted from 15M research publications.

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 several semantically enhanced solutions, such as Smart Topic Miner, a web application that supports editors in classifying books with relevant metadata, and Smart Book Recommender, a system that assists editors in deciding which products should be marketed at scientific venues.

In 2019, we released the Computer Science Ontology (CSO), which is currently the largest taxonomy of research areas in the field and has been officially adopted by Springer Nature.  In the context of our collaboration with Springer Nature, I have also designed and co-developed the Smart Topic Miner, a tool is used by editors at Springer Nature to generate automatically the scholarly metadata for all their computer science proceedings, including flagship series, such as Lecture Notes in Computer Science (LNCS), Lecture Notes in Artificial Intelligence, and others.

In 2021, we generated the Academia/Industry DynAmics (AIDA) Knowledge Graph, an innovative resource for supporting large-scale analyses of research trends across academia and industry.  AIDA describes 14M publications and 8M patents according to the research topics drawn from CSO, the type of the author’s affiliations (e.g., academy, industry, collaborative), and 66 industrial sectors (e.g., automotive, financial, energy, electronics). In 2022, we also produced the Computer Science Knowledge Graph (CS-KG), a large-scale automatically generated knowledge graph that describes 25M entities (e.g., tasks, methods, metrics, materials, others) relevant to AI extracted from 15M articles. CS-KG was designed to support a large variety of intelligent services for analysing and making sense of research dynamics, assisting researchers, and informing the decisions of founding bodies and research policymakers.

Recently, our work has focused also on the intersection of generative AI and knowledge graphs, aiming to create advanced systems for scientific exploration, horizon scanning, research hypothesis generation, and the automated production of literature reviews.