Document - Detection, analysis, and prediction of research topics with scientific knowledge graphs

2021

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Detection, analysis, and prediction of research topics with scientific knowledge graphs

Salatino A., Mannocci A., Osborne F.

Scientific Knowledge Graphs Scientometrics Bibliometrics Prediction Research topics

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.

Source: Predicting the Dynamics of Research Impact, edited by Manolopoulos Y., Vergoulis T., pp. 225–252, 2021

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@inbook{oai:it.cnr:prodotti:465887,
	title = {Detection, analysis, and prediction of research topics with scientific knowledge graphs},
	author = {Salatino A. and Mannocci A. and Osborne F.},
	doi = {10.1007/978-3-030-86668-6_11},
	booktitle = {Predicting the Dynamics of Research Impact, edited by Manolopoulos Y., Vergoulis T., pp. 225–252, 2021},
	year = {2021}
}