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.

Salatino, A., Mannocci, A., Osborne, F. (2021). Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs. In Manolopoulos Y, Vergoulis T (a cura di), Predicting the Dynamics of Research Impact. (pp. 225-252). Springer International Publishing - Springer Nature [10.1007/978-3-030-86668-6_11].

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

Osborne F
2021

Abstract

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.
Capitolo o saggio
detection;
English
Predicting the Dynamics of Research Impact.
978-3-030-86667-9
Salatino, A., Mannocci, A., Osborne, F. (2021). Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs. In Manolopoulos Y, Vergoulis T (a cura di), Predicting the Dynamics of Research Impact. (pp. 225-252). Springer International Publishing - Springer Nature [10.1007/978-3-030-86668-6_11].
Salatino, A; Mannocci, A; Osborne, F
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/381617
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