Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs
This work addresses the need for data-driven insights into research dynamics to inform funding and technology decisions in academia and industry, though it appears incremental as it builds on existing knowledge graphs and ontologies.
The authors tackled the problem of analyzing and forecasting research trends by developing a framework based on a large-scale scientific knowledge graph, using the Computer Science Ontology to characterize articles and produce bibliometric studies and predictive tools.
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.