UniSkill: A Dataset for Matching University Curricula to Professional Competencies
This work addresses a gap in skill extraction for education and job matching by providing a public dataset, though it is incremental as it builds on existing methods for a specific domain.
The authors tackled the scarcity of datasets for matching university curricula to professional skills by releasing UniSkill, a dataset with manual and synthetic annotations linking courses to ESCO taxonomy skills, and trained BERT models that achieved an 87% F1-score, demonstrating feasibility for retrieval and recommendation tasks.
Skill extraction and recommendation systems have been studied from recruiter, applicant, and education perspectives. While AI applications in job advertisements have received broad attention, deficiencies in the instructed skills side remain a challenge. In this work, we address the scarcity of publicly available datasets by releasing both manually annotated and synthetic datasets of skills from the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy and university course pairs and publishing corresponding annotation guidelines. Specifically, we match graduate-level university courses with skills from the Systems Analysts and Management and Organization Analyst ESCO occupation groups at two granularities: course title with a skill, and course sentence with a skill. We train language models on this dataset to serve as a baseline for retrieval and recommendation systems for course-to-skill and skill-to-course matching. We evaluate the models on a portion of the annotated data. Our BERT model achieves 87% F1-score, showing that course and skill matching is a feasible task.