Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detection, Analysis, and Prediction of Research Topics with Scientific
  Knowledge Graphs   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.

31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an