DataOps for Societal Intelligence - a Data Pipeline for Labor Market Skills Extraction and Matching

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI)
Pages391-394
Number of pages4
ISBN (Electronic)9781728110547
DOIs
Publication statusPublished - Aug 2020

Bibliographical note

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.

Fingerprint

Dive into the research topics of 'DataOps for Societal Intelligence - a Data Pipeline for Labor Market Skills Extraction and Matching'. Together they form a unique fingerprint.

Cite this