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 covers Artificial Intelligence, Information Extraction, Knowledge Graphs, Science of Science, Semantic Web, Research Analytics, and Semantic Publishing. I have authored more than eighty peer-reviewed publications in top journals and conferences in my research areas, including the Semantic Web Journal, ISWC, ESWC, WebConf, JCDL, TPDL, UMAP, Data Science, Data Intelligence, and the International Journal of Human-Computer Studies. I regularly organize scientific events and special issues on these topics. Most recently I chaired the Workshop on Deep Learning For Knowledge Graph (DL4KG at ESWC 2020), the Workshop on Scientific Knowledge Graphs (SKG at TPDL 2020), and acted as guest editor for two special issues of the Semantic Web Journal and Quantitative Science Studies. I am also a member of the Editorial Board of the Data Intelligence Journal and the Knowledge Graph Construction W3C Community Group.
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 Knowledge Graph (AIDA) , 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.