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.

翻译：分析研究趋势并预测其对学术界和产业的影响,对于更深入地了解研究领域的进展和为关于研究资金和技术采用的重要决定提供信息至关重要。在过去几年中,我们看到出现了一些公开的和大规模的科学知识图表,促进开发许多数据驱动的方法,对研究趋势进行定量分析。本章提供了一个创新框架,用于根据计算机科学本体学的研究专题,根据大规模知识图表,探测、分析和预测研究专题,将研究文章定性为研究文章。我们讨论了基于正式专题表述的解决办法的优点,并介绍了如何应用这些解决方案来产生计量研究以及分析和预测研究动态的创新工具。