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.

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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},
	year = {2021}
}