TY - GEN
T1 - DataOps for Societal Intelligence - a Data Pipeline for Labor Market Skills Extraction and Matching
AU - Tamburri, Damian A.
AU - Heuvel, Willem-Jan van den
AU - Garriga, Martin
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2020/8
Y1 - 2020/8
N2 - Big Data analytics supported by AI algorithms enable skills localization and retrieval, in the context of a labor market intelligence problem. We formulate and solve this problem through specific DataOps models, blending data sources from administrative and technical partners in several countries into cooperation, creating shared knowledge to support policy and decision-making. We then focus on the critical task of skills extraction from resumes and vacancies featuring state-of-The-Art machine learning models. We showcase preliminary results with applied machine learning on real data from the employment agencies of the Netherlands and the Flemish region in Belgium. The final goal is to match these skills to standard ontologies of skills, jobs and occupations.
AB - Big Data analytics supported by AI algorithms enable skills localization and retrieval, in the context of a labor market intelligence problem. We formulate and solve this problem through specific DataOps models, blending data sources from administrative and technical partners in several countries into cooperation, creating shared knowledge to support policy and decision-making. We then focus on the critical task of skills extraction from resumes and vacancies featuring state-of-The-Art machine learning models. We showcase preliminary results with applied machine learning on real data from the employment agencies of the Netherlands and the Flemish region in Belgium. The final goal is to match these skills to standard ontologies of skills, jobs and occupations.
UR - http://www.scopus.com/inward/record.url?scp=85092186447&partnerID=8YFLogxK
U2 - 10.1109/IRI49571.2020.00063
DO - 10.1109/IRI49571.2020.00063
M3 - Conference contribution
SP - 391
EP - 394
BT - 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI)
ER -