Ontology enhanced representing and reasoning of job specific knowledge to identify skill balance

The modernized and knowledge-based world of work (WoW) requires well-educated, skillful, and competent employees, who demonstrate the expected quality performance on the job. To supply knowledge, skills, and competences (KSCs) de-manded by the WoW, vocational education and training (VET) systems are established. VET is understood as a demand-driven education sector in the world of education (WoE). On the premise that WoE supplies what is demanded by WoW, we may ap-proach the problem of skill imbalance and mismatches not only on the micro level but also on the macro level.
The micro level matching determines whether a KSC possessed by a job seeker/an employee corresponds to KSCs required by an employer or if there is a KSC imbalance problem. The macro level skill matching is individual-independent i.e. between the learning outcomes of the learning fields supplied by the WoE and KSCs demanded by the WoW to perform the tasks of the job. The result of matching identifies to what ex-tent the WoE can satisfy the demand of the WoW for qualified job applicants who pos-sess the required KSCs. Consider the matching of the KSCs supplied by a learning field and the demanded KSCs for a job, the qualitative analysis results in five states: gap, shortage, surplus, obsolete and balance.
One way to reduce the skill imbalance is the (re)training of job-learners and/or on the job training of employees to develop or maintain the demanded KSCs. For this pur-pose and prior to initiating any training program, what is demanded to be learned should be identified. To do so, there is a need to establish a communication channel between WoW and WoE, which facilitates the detection of the imbalance between the supplied learning outcomes and demanded KSCs.
Taking the problem of supply-demand imbalance into account, the present thesis contributes in three dimensions. First, introducing and conceptualizing the communica-tion channel and the matching space known as the world of competence (WoC). Sec-ond, semantic representation of the matching process by constituting the model of Job-Know Ontology, which provides a shared understanding and interpretation from the matching state. In order to assure the usability of the Job-Know Ontology especially for non-technical target groups in the WoW and WoE, developing the ontology shall con-front a great challenge with regard to social quality and maturity. Third, formalizing and realizing the Job-Know Ontology, consisting of the two domains of WoW and WoE, as a generic solution not only to represent knowledge of the fields but also to support in-ferences and semantic reasoning (i.e. semantic matching of WoW and WoE).
In the light of this fact, the main result of the present thesis is an ontology called Job-Know Ontology as a representation of two interdisciplinary domains, WoW and WoE, to provide one picture by focusing on their melting point, which creates the WoC. The Job-Know Ontology provides novel mechanisms to infer the KSC states, which the labor market may confront, by matching the job tasks and the learning units of the field via supplied and demanded KSCs. Last but not least, the instantiation of the proposed model has been investigated and resulted in the development and evaluation of Nursing Job-Know Ontology. In addition, the degree of domain-independency of the proposed model has been examined through the realization of Production-Logistics Job-Know Ontology.