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

arXiv: https://arxiv.org/abs/2106.12875 · HTML: https://mlsanity.com/paper/2106.12875/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2021.

## Built on by (most cited first, 2 of 2)

- [Knowledge Graph Induction enabling Recommending and Trend Analysis: A Corporate Research Community Use Case](https://mlsanity.com/paper/2207.05188/) (2022)
- [COCI: Conference Organisers and Content Identifier](https://mlsanity.com/paper/2608.24559/) (2026)

## Abstract (arXiv)

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.