Improve the matching between vacancies from companies and job seekers by developing an AI demonstrator that provides vacancy scores and recommendations of skills changes.


Skills Matching was a one-year project completed that have been carried out in 2020. This project was a use-case project of Appl.AI, in which TNO collaborated with experts from are UWV (Employee Insurance Agency Netherlands), CBS (Statistics Netherlands) and CPB (Netherlands Bureau for Economic Policy Analysis).. The ultimate ambition is to better match job seekers with vacancies. We use a skills-based approach, which means that matching takes place based on skills that a candidate has and skills that are required for a particular job.

The project is relevant, because currently there is still a significant mismatch between supply-and-demand in the labor market. It is recognized that matching based on skills helps to reduce the mismatches. There are various international skills classifications, but these do not provide insight into changes in the Dutch labor market, while this is important in the changing labor environment. During (and after) this current COVID-19 period, the need for a dynamical (and bias-free) skills approach has even become more urgent, as the number of job seekers will grow and the matches with adequate jobs will be more challenging.

Figure showing two people seated opposite each other at a table with a sheet of paper

Problem description

A step towards the implementation of the above ambition is to innovate along different axes, with the following research questions:

  • How does the Hybrid-AI solution look like for the dynamic skills ontology?

-- How can the different international skills taxonomies/ontologies be matched and be connected to UWV’s CompetentNL? -- How does the roadmap look like towards an implementation of a dynamic skills ontology (for example in CompetentNL)?

  • How to improve AI tools for a better skills matching?

-- Which AI methods can be used to improve the algorithm for extraction skills from vacancies? -- Which AI methods can be used to extract skills from vacancy texts? -- What (AI) components should be included to score vacancies?

  • How to do bias-mitigation based on AI in vacancy texts?

-- How to develop an appropriate bias-mitigation tool, so that the skills ontology and vacancy text are less biased and the corresponding skills matching can be done in a fairer way?

## Research approach The research questions are examined in separate work packages. Each work package has its own focus, ranging from projecting terms on top of each other to creating a demonstrator that helps to improve job vacancy texts. Figure showing the different project work packages

The work has been carried out in an Agile/Scrum way-of-working. In total, there were 5 sprints with a duration of approximately a month per sprint. At the beginning of each sprint, the priorities of the activities are discussed with the product owner and the stakeholders. Then, the scrum master facilitate the further process in the sprint with the development team. At the end of each sprint, the results are presented during the sprint review, where all stakeholders are invited to join.


A dynamic skills ontology has been developed, based on current information about the skills required in vacancies and the adoption of AI techniques. Moreover, bias-identification tools have been developed with the help of AI, so that bias in skills ontology and vacancy text can be identified and resolved. We have built an online demonstrator, where the potential of the different research directions are shown.

A first version of the project demonstrator has been delivered. The demonstrator can be found online, see the link below.

Figure showing a screenshot of the projects interactive demonstrator

Future work: Skills Matching 2.0

In “Skills Matching 2.0”, we would like to continue our research activities on AI innovations for matching job seekers and vacancies. The external stakeholders are UWV, CBS and CPB. The major end-user is UWV, since they need these innovations for improving their core task regarding employment. In addition, most likely we will have new external stakeholders.

In Skills Matching 2.0, we will focus on the further development of the dynamic skills ontology including its explainability, further research on bias mitigation in skills and vacancy text, and improve the project demonstrator so that it is more robust and closer to what is needed in labor-market practice.


  • Jok Tang, Sr Consultant, TNO, e-mail:
  • Joost Genabeek, Sr Research Scientist, TNO, e-mail: