Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs

“Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs” is a book chapter of “Predicting the Dynamics of Research Impact” edited by Springer.

Angelo A. Salatino1, Andrea Mannocci2, and Francesco Osborne1

1Knowledge Media Institute – The Open University, Milton Keynes, United Kingdom

2Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo”, Italian National Research Council, Pisa, Italy


Analysing research trends and predicting their impact on academia and industry is crucial to gain a deeper understanding of the advances in a research field and to inform critical decisions about research funding and technology adoption. In the last years, we saw the emergence of several publicly-available and large-scale Scientific Knowledge Graphs fostering the development of many data-driven approaches for performing quantitative analyses of research trends. This chapter presents an innovative framework for detecting, analysing, and forecasting research topics based on a large-scale knowledge graph characterising research articles according to the research topics from the Computer Science Ontology. We discuss the advantages of a solution based on a formal representation of topics and describe how it was applied to produce bibliometric studies and innovative tools for analysing and predicting research dynamics.


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