As a result of transformation processes, the German labor market is highly dependent on vocational training, retraining and continuing education. To match training seekers and offers, we present a novel approach towards the automated detection of access to education and training in German training offers and advertisements. We will in particular focus on (a) general school and education degrees and schoolleaving certificates, (b) professional experience, (c) a previous apprenticeship and (d) a list of skills provided by the German Federal Employment Agency. This novel approach combines several methods: First, we provide a mapping of synonyms in education combining different qualifications and adding deprecated terms. Second, we provide a rule-based matching to identify the need for professional experience or apprenticeship. However, not all access requirements can be matched due to incompatible data schemata or non-standardizes requirements, e.g initial tests or interviews. While we can identify several shortcomings, the presented approach offers promising results for two data sets: training and re-training advertisements.

At the end of the week I had a chance to give a guest input at the kick-off meeting of the new Erasmus Plus project "Artificial Intelligence (AI) and Vocational Education and Training (VET)". The partners come from Greece, Italy, Lithuania and the United Kingdom/Wales. All partners are known to us from previous European cooperation activities, so the project team was in a good position to have a rapid start. My role as a visitor was to give an overview of some predecessor projects and their recent/ongoing work. In addition I had a surprise input to deliver on top of my presentation.