DataOps for Societal Intelligence: a Data Pipeline for Labor Market Skills Extraction and Matching
This work addresses labor market intelligence for policy and decision-making, but it appears incremental as it applies existing methods to new data.
The paper tackles the problem of extracting and matching labor market skills from resumes and vacancies using DataOps models and state-of-the-art machine learning, showcasing preliminary results on real data from the Netherlands and Belgium.
Big Data analytics supported by AI algorithms can support 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.