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.

Giabelli, A., Malandri, L., Mercorio, F., Mezzanzanica, M. (2020). GraphLMI: A data driven system for exploring labor market information through graph databases. MULTIMEDIA TOOLS AND APPLICATIONS [10.1007/s11042-020-09115-x].

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

Giabelli, Anna
;
Malandri, Lorenzo;Mercorio, Fabio
;
Mezzanzanica, Mario
2020

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.
Articolo in rivista - Articolo scientifico
Graph database, Labor market intelligence, Social networking, Web data
English
Giabelli, A., Malandri, L., Mercorio, F., Mezzanzanica, M. (2020). GraphLMI: A data driven system for exploring labor market information through graph databases. MULTIMEDIA TOOLS AND APPLICATIONS [10.1007/s11042-020-09115-x].
Giabelli, A; Malandri, L; Mercorio, F; Mezzanzanica, M
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/277985
Citazioni
  • Scopus 11
  • ???jsp.display-item.citation.isi??? 10
Social impact