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GraphLMI: A data driven system for exploring labor market information through graph databases

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Abstract

Labor Market Intelligence (LMI) is an emerging field of study that has been gaining interest as it allows employing Artificial Intelligence (AI) algorithms on labor market information. The goal of LMI is to support decision and policy making activities (e.g., real-time monitoring of Online Job Vacancies (OJV) across countries, forecast skill requested within vacancies, compare similar labor markets across borders, etc.). The European project in which this work is framed can be placed in this field, as it aims at collecting and classifying millions of OJVs from 28 EU Countries, handling 32 languages, and also extracting the requested skills. The result is a huge amount of information useful for understanding labor market dynamics and trends. The goal of this work is to realize a system - namely GraphLMI - that organizes such Labor Market information as a graph, enabling the representation of occupation/skill relevance and similarity over the European Labor Market; another goal is to enrich the European standard taxonomy of occupations and skills (ESCO) to better fit the labor market expectations. We formalize and design the GraphLMI data model, then we implement it as a graph-database, generated by processing 5.3+ million OJVs composed by free text and collected between 2018 and 2019 for France, Germany, and the United Kingdom. Finally, we show how the resulting knowledge can be queried through a declarative query language to understand, compare and evaluate country-based labor market dynamics for supporting policy and decision making activities at European level.

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Notes

  1. The European Centre for the Development of Vocational Training

  2. An Online Job Vacancy (OJV) is a Web page containing a title - that shortly summarizes the job position - and a full description, usually used to advertise the skills a candidate should hold

  3. Notice that occupation and skill taxonomies act like dictionaries, hence they do not provide the importance of occupations and skills

  4. The European Skill, Competences, Qualifications and Occupations https://ec.europa.eu/esco/portal

  5. Publicly available at https://goo.gl/Goluxo

  6. The Commission Communication “A New Skills Agenda for Europe” COM(2016) 381/2, available at https://goo.gl/Shw7bI

  7. O*net is on its own an extension of the Standard Occupational Classification (SOC) system developed by the U.S. Bureau of Labor Statistics

  8. http://www.burning-glass.com/

  9. http://www.wollybi.com

  10. https://www.gartner.com/en/human-resources/research/talentneuron

  11. http://www.workday.com/

  12. https://www.pluralsight.com/

  13. http://www.employinsight.com/

  14. http://www.textkernel.com/

  15. https://cloud.google.com/jobs-api/

  16. A multi-graph is a graph where multiple edges between two nodes are permitted and might be specified through labels

  17. https://tinyurl.com/sv4squr

  18. See https://ec.europa.eu/esco/resources/data/static/model/html/model.xhtml for details about how ESCO is organized

  19. https://www.onetonline.org/

  20. https://tinyurl.com/sv4squr

  21. https://tinyurl.com/w5h35c4

  22. https://tinyurl.com/sxejkq3

  23. https://tinyurl.com/qnzxccj

  24. https://tinyurl.com/r53r798

  25. As term to calculate the term frequency we consider respectively skills, cities, sectors, sites and educational levels.

  26. The idea is inspired by [3] though they compute the weight of triples through arithmetic functions

  27. https://goo.gl/5FZS3E

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Acknowledgments

This work is partially supported within the research activity of a EU Project entitled ”Real-time Labor Market information on Skill Requirements: Setting up the EU system for online vacancy analysisFootnote 27 in which some of the authors are involved as coordinators and researchers.

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Authors and Affiliations

  1. CRISP Research Centre, University of Milano Bicocca, Milan, Italy

    Anna Giabelli, Lorenzo Malandri, Fabio Mercorio & Mario Mezzanzanica

  2. Department of Statistics and Quantitative Methods, University of Milano Bicocca, Milan, Italy

    Lorenzo Malandri, Fabio Mercorio & Mario Mezzanzanica

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  1. Anna Giabelli
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  2. Lorenzo Malandri
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  3. Fabio Mercorio
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  4. Mario Mezzanzanica
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Correspondence to Anna Giabelli.

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Giabelli, A., Malandri, L., Mercorio, F. et al. GraphLMI: A data driven system for exploring labor market information through graph databases. Multimed Tools Appl 81, 3061–3090 (2022). https://doi.org/10.1007/s11042-020-09115-x

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