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

Salatino, Angelo A.; Mannocci, Andrea and Osborne, Francesco (2021). Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs. In: Manolopoulos, Yannis and Vergoulis, Thanasis eds. Prediction Dynamics of Research Impact. Springer, pp. 225–252.

DOI: https://doi.org/10.1007/978-3-030-86668-6_11

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.