Translating Job Advertisements into Competency Taxonomy: An Interpretable Approach for Robotics Recruitment Analysis

To translate robotics recruitment texts into an interpretable competency taxonomy, this study explored how latent topics can be induced from large-scale job advertisements and used to construct a structured competency taxonomy. A domain-specific preprocessing pipeline was applied to a corpus of robotics job advertisements, and latent topics were subsequently induced using Latent Dirichlet Allocation (LDA). Building on the extracted topic evidence, a procedure was applied to organize topic summaries into task domains and competencies grounded in topic keywords. Evaluation was conducted using a set of quantitative measures to assess semantic consistency and structural quality. The results revealed recurring competency patterns encompassing on-site operation and service, engineering design and integration, software and algorithm development, and system verification and reliability assurance. The resulting competency taxonomy captures the underlying structure of employer demand and provides an interpretable basis for robotics skill demand analysis, supporting role profiling and serving as an empirical reference for Vocational Education and Training (VET).