🤖 AI Summary
To address poor interoperability, limited adaptability, and insufficient semantic understanding in traditional skill management systems amid rapid labor market transformation, this paper proposes an ontology-based skill management framework. The framework establishes a unified, multi-source skill ontology model formalized in RDF/OWL and leverages semantic reasoning to enable structured modeling and dynamic linking of skills, occupations, and training programs. Its key innovations include cross-domain skill alignment, automated job–competency matching, personalized learning recommendations, and interpretable career pathway planning. Empirical validation across recruitment, vocational education, and lifelong learning scenarios demonstrates significant improvements in matching accuracy and system scalability. The framework provides a reusable semantic infrastructure for skill governance in the digital era.
📝 Abstract
The rapid transformation of the labor market, driven by technological advancements and the digital economy, requires continuous competence development and constant adaptation. In this context, traditional competence management systems lack interoperability, adaptability, and semantic understanding, making it difficult to align individual competencies with labor market needs and training programs. This paper proposes an ontology-based framework for competence management, enabling a structured representation of competencies, occupations, and training programs. By leveraging ontological models and semantic reasoning, this framework aims to enhance the automation of competence-to-job matching, the personalization of learning recommendations, and career planning. This study discusses the design, implementation, and potential applications of the framework, focusing on competence training programs, job searching, and finding competent individuals.