ESCO is a multilingual classification of Skills, Competences, Qualifications, and Occupations created by the European Commission to improve the supply of information on skills demand in the labour market. It is designed to assist individuals, employers, universities and training providers by giving them up to date and standardized information on skills. Rapid technological change means that ESCO needs to be updated in a timely manner. Evidence is presented here of how text-mining techniques can be applied to the analysis of data on emerging skill needs arising from Industry 4.0 to ensure that ESCO provides information which is current. The alignment between ESCO and Industry 4.0 technological trends is analysed. Using text mining techniques, information is extracted on Industry 4.0 technologies from: (i) two versions of ESCO (v1.0 - v1.1.); and (ii) from the 4.0 related scientific literature. These are then compared to identify potential data gaps in ESCO. The findings demonstrate that text mining applied on scientific literature to extract technology trends, can help policy makers to provide more up-to-date labour market intelligence.

Towards ESCO 4.0 – Is the European classification of skills in line with Industry 4.0? A text mining approach

Chiarello, Filippo
Primo
;
Fantoni, Gualtiero;Giordano, Vito;Spada, Irene
2021-01-01

Abstract

ESCO is a multilingual classification of Skills, Competences, Qualifications, and Occupations created by the European Commission to improve the supply of information on skills demand in the labour market. It is designed to assist individuals, employers, universities and training providers by giving them up to date and standardized information on skills. Rapid technological change means that ESCO needs to be updated in a timely manner. Evidence is presented here of how text-mining techniques can be applied to the analysis of data on emerging skill needs arising from Industry 4.0 to ensure that ESCO provides information which is current. The alignment between ESCO and Industry 4.0 technological trends is analysed. Using text mining techniques, information is extracted on Industry 4.0 technologies from: (i) two versions of ESCO (v1.0 - v1.1.); and (ii) from the 4.0 related scientific literature. These are then compared to identify potential data gaps in ESCO. The findings demonstrate that text mining applied on scientific literature to extract technology trends, can help policy makers to provide more up-to-date labour market intelligence.
2021
Chiarello, Filippo; Fantoni, Gualtiero; Hogarth, Terence; Giordano, Vito; Baltina, Liga; Spada, Irene
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1107026
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