The present technical report provides an in-depth explanation of the work performed to produce a crosswalk between ESCO, the European multilingual classification of Skills, Qualifications, and Occupations, developed by DG Employment, Social Affairs and Inclusion of the European Commission, and O*NET, the Occupational Information Network, developed by the U.S. Department of Labor/Employment and Training Administration (US DOL).

In the past, crosswalks were created only sporadically, as they require a remarkable effort in terms of time and resources. Today, new technologies such as Machine Learning and Natural Language Processing significantly reduce the effort required to carry out such activities. This report shows the result of the work done to connect the ESCO and O*NET classifications using machine learning models and human validation.

After describing the two classifications, the report clarifies the purpose of the crosswalk, details the methodology used to map the ESCO classification to O*NET, and concludes with the results of the mapping.