Automatically Learning a Human-Resource Ontology from Professional Social-Network Data

TitreAutomatically Learning a Human-Resource Ontology from Professional Social-Network Data
Type de publicationConference Paper
Année de publication2019
AuteursAlfonso-Hermelo, D., P. Langlais, and L. Bourg
ÉditeurMeurs, M-J., and F. Rudzicz
Nom de la conférenceAdvances in Artificial Intelligence
ÉditeurSpringer International Publishing
Endroit d'éditionCham
ISBN Number978-3-030-18305-9
RésuméIn this work, we build an ontology (automatically learned) in the domain of Human Ressources by using a simple, efficient and undemanding procedure. Our principal challenge is to tackle the problem of automatically grouping human-provided job titles into a hierarchy and by similarity (as they are presented in human-made HR ontologies). We use the Louvain algorithm, a greedy optimization method that, given a sufficient amount of data, interconnects domain-specific jobs that have more skills in common than jobs from different domains. In our case, we used publicly available profiles from LinkedIn (written in English by users in France). An automatic evaluation was performed and shows that the resulting ontology is similar in size and structure to ESCO (one of the most complete human-made ontology for HR). The whole procedure allows recruitment professionals to easily generate and update this ontology with virtually no human intervention.