An Ontology-Based Approach for the Semantic Representation of Job Knowledge
Marjan Khobreh, Fazel Ansari, Madjid Fathi, Réka Franciska Vas, Stefan T. Mol, Hannah Ariane Berkers, K. Varga · IEEE Transactions on Emerging Topics in Computing · 2015
The essential and significant components of one's job performance, such as facts, principles, and concepts are considered as job knowledge. This paper provides a framework for forging links between the knowledge, skills, and abilities taught in vocational education and training (VET) and competence prerequisites of jobs. Specifically, the study is aimed at creating an ontology for the semantic representation of that which is taught in the VET, that which is required on the job, and how the two are related. In particular, the creation of a job knowledge (Job-Know) ontology, which represents task and knowledge domains, and the relation between these two domains is discussed. Deploying the Job-Know ontology facilitates bridging job and knowledge elements collected from various sources (such as job descriptions), the identification of knowledge shortages and the determination of mismatches between the task and the knowledge domains that, in a broader perspective, facilitate the bridging requirements of labor market and education systems.