I am a Senior 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 covers Artificial Intelligence, Information Extraction, Knowledge Graphs, Science of Science, Semantic Web, Research Analytics, and Semantic Publishing. I have authored more than a hundred peer-reviewed publications in top journals and conferences in my research areas, including the Semantic Web Journal, Neurocomputing, Future Generation Computer Systems, the International Journal of Human-Computer Studies, ISWC, ESWC, WebConf, JCDL, TPDL, and UMAP. I won several awards, including the Best In-Use Paper Award at the International Semantic Web Conference 2022, the Best Demo Award the International Semantic Web Conference 2020, and the Semantic Publishing Award at European Semantic Web Conference 2014. I regularly organize scientific events and special issues. Most recently I chaired the Workshop on Scientific Knowledge (Sci-K at TheWebConf 2022), the Workshop on Deep Learning for Knowledge Graphs (DL4KG at ISWC 2022), and acted as guest editor for two special issues of the Semantic Web Journal and Quantitative Science Studies.
The SKM team aims to produce 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 used by editors at Springer Nature to automatically generate 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 released the Academia/Industry DynAmics Knowledge Graph (AIDA) , an innovative resource for supporting large-scale analyses of research trends across academia and industry. AIDA describes 25M 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 2022, we released the Computer Science Knowledge Graph (CS-KG), a knowledge base that describes 10M methods, tasks, materials, and metrics automatically extracted from 7M computer science 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 decisions of funding bodies and research policymakers.
For recent news refer to the Scholarly Knowledge Mining team site.
You can find more info on my resume.