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

TitleAutomatically Learning a Human-Resource Ontology from Professional Social-Network Data
Publication TypeConference Paper
Year of Publication2019
AuthorsAlfonso-Hermelo, D., P. Langlais, and L. Bourg
EditorMeurs, M-J., and F. Rudzicz
Conference NameAdvances in Artificial Intelligence
PublisherSpringer International Publishing
Place PublishedCham
ISBN Number978-3-030-18305-9
AbstractIn 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.