About me

Francesco Osborne

I am a Research Fellow at the Knowledge Media institute of the Open University in Milton Keynes, UK, where I lead the Scholarly Knowledge Mining (SKM) team.  My research focuses on Artificial Intelligence, Information Extraction, Knowledge Graphs, Science of Science, Semantic Web, Research Analytics, and Semantic Publishing. Since joining KMi in 2013, I have contributed, either as PI, Co-PI, or Technical Director to bringing in over £700K in funding. I have authored more than seventy peer-reviewed publications in top journals and conferences in my areas of research, including the Semantic Web Journal, ISWC, ESWC, WebConf, JCDL, TPDL, UMAP, Data Science, Data Intelligence, and the International Journal of Human-Computer Studies.

The SKM team aims at producing innovative approaches leveraging large-scale data mining, semantic technologies, machine learning and visual analytics for making sense of scholarly data and forecast research dynamics. We collaborate with a number of commercial organizations (e.g., Springer Nature, Elsevier, Microsoft, Digital Science, Figshare), non-profit organizations, and universities.

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 2020, we released 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 the Computer Science Ontology, the type of the author’s affiliations (e.g., academy, industry, collaborative), and 66 industrial sectors (e.g., automotive, financial, energy, electronics). In the same year we also produced the Artificial Intelligence Knowledge Graph (AI-KG), a large-scale automatically generated knowledge graph that describes 850K entities (e.g., tasks, methods, metrics, materials, others) relevant to AI according to 1,2M statements extracted from 333K articles. AI-KG was designed to support a large variety of intelligent services for analysing and making sense of research dynamics, assisting researchers, and informing decision of founding bodies and research policy makers.

Currently, I am co-ordinating the process of adapting our technologies for use in the Biomedical fields and, in this context, we are contributing to the large scientific effort around COVID-19 by extracting key medical concepts from a large collection of scientific articles.

For recent news refer to the Scholarly Knowledge Mining team site.

You can find more info on my resume.