January 24, 2023

An ontology-based skill recommender system for job matching

In the context of the EUH4D project, BlockchainX AB, a Swedish tech startup, cooperates with ahedd, AIR4S and CeADAR DIHs in order to develop a skill recommender system to contribute to a common European skills data space to reduce the skills mismatches between the education and training system on the one hand and the labor market needs on the other. With its job-matching app Employchain, the company aims to facilitate data-driven matchmaking of skills supply and demand in the European labor market.

The Problem

Almost 50% of adult workers in the EU are not satisfied or only moderately satisfied with their qualifications and skills job match in the labor market, according to CEDEFOP’s 2021 European skills and jobs survey. Moreover, the COVID-19 pandemic has accelerated the skills mismatch in the EU, not only because of the higher dynamics of structural change, but also because of the turn of mindset that existentially affected workers have undergone.

The Solution

Employchain is a Tinder-like job matching app from Sweden that matches skills supply and demand in the labor market. In this experiment, a skill recommender system is developed which is based on a knowledge graph that is enriched with all Swedish higher education learning opportunities and their learning outcomes mapped to ESCO skills. This enables much more accurate representation and matching of education-related CV data.

The main innovation is the development of a knowledge graph that integrates learning opportunities by the Swedish National Agency for Education and their learning outcomes defined by the different education providers into a common ontology based on ESCO skills. The new ontology enables the construction of a skill recommender system that identifies the skills and knowledge a learner has acquired through their qualification.

Employchain aims to improve awareness and common understanding of skills terminology in the education sector and labor market across Europe. The long-term vision is to create an automated and transparent matching ecosystem where skills supply and demand are represented on a knowledge graph in real-time, empowering stakeholders to adapt to each other’s needs more quickly and accurately.


How ahedd contributed to this case

In this experiment, ahedd cooperates with AIR4S in the construction of the Knowledge Graph which is the basis of the backend of the application. Then, ahedd offers the triple store database provisioning and the query interface for the aforementioned Knowledge Graph.

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