An Ontology-Based Recommendation Module for Optimal Career Choices

The current paper describes an innovative career recommendation module embedded into a web platform developed with emergent technologies. The recommendation algorithm bases its strength on semantic data and on HermiT inference capabilities. An ontology aligned with well-known occupational classifications, such as ESCO, O*NET or COR is the core of the recommendation process, which was designed to especially support students who are in high school, college or have recently graduated university, with no work experience so far and no capacity to match the occupational nomenclature from the labor market with their own competencies and profile. To perform the ontological inference-based recommendation, the user profile must be created, using a form-extraction mechanism which provides relevant educational background and psychological traits. Due to the current technological revolution and to the continuous change in the range of professional occupations, our proposed recommender has become a useful career guidance tool, fact supported by preliminary user testing experiments.