ETHAN
At present, there is no system that is capable of normalizing the capacities described by candidates and claimants to a common ontology. In other words, the descriptions of each of the parties involved in the process can mean different things, which is why it is almost impossible to match supply with demand in an agile and automated way. This, on the other hand, makes it impossible to establish metrics for analyzing the labor market in terms of the real need for specific skills, the average compensation for these skills, and even the location and geographical distribution of this supply/demand. In other words, few analyses can be carried out to support decision-making both for job seekers and providers.
To address the numerous challenges currently faced in matching supply and demand, an automatic system has been developed capable of classifying skills with the objective of simplifying the search for job and training opportunities, effectively connecting supply and demand. Using the European ontology ESCO, LLMs, and various NLP models, this system identifies, processes, and analyzes skills drawn from various sources such as job offers, CVs, and LinkedIn profiles. The objective is to remain up to date and efficient in an international and dynamic environment through constant monitoring and retraining of models as necessary.
ETHAN facilitates a better match between candidates and job opportunities, thus optimizing the recruitment process and reducing the time needed to find an ideal candidate. It also improves access to relevant training programs, allowing candidates and organizations to develop critical skills more effectively. In addition, by being automatically updated, the system ensures its applicability and usefulness in a labor market that is constantly changing, contributing to greater efficiency in the employment and training ecosystem.