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

TL;DR

This paper introduces a framework utilizing large-scale scientific knowledge graphs to detect, analyze, and forecast research topics, aiding understanding of research trends and informing strategic decisions in academia and industry.

Contribution

It presents a novel framework that leverages formal representations of research topics within knowledge graphs for trend detection and prediction, applied to bibliometric studies and research dynamics analysis.

Findings

Effective detection of research topics using knowledge graphs

Successful forecasting of research trend evolution

Enhanced tools for research analysis and prediction

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.