In this blog series, international experts on the topic of analyzing online job advertisements present their work. The presentations were given at the 7th OJA Forum, which is jointly organized twice a year by the Federal Institute for Vocational Education and Training and the Bertelsmann Stiftung. Topics at the 7th OJA Forum included the visualization and use of data using dashboards and the assignment of job titles to online job advertisements using various methodological approaches. This article is about the contribution of Kasper Kok from Textkernel.

Profession classification in the messy real world: methodology and challenges

Job titles in online job ads come in many forms of wording and granularity. Therefore, standardization to a consistent catalogue or taxonomy is a prerequisite to effective analytics and matching. Most public taxonomies are created in a ‚top-down‘ fashion and don’t reflect the variety of job titles as seen in OJAs. Textkernel maintains a ‚bottomup‘ professions taxonomy instead, for which CV and job ad data is the starting point. In this presentation, Kasper Kok will discuss aspects of the methodology for creating this taxonomy and keeping it up to date and aligned across languages. He discusses some of the main challenges, such as dealing with differences in granularity, seniority distinctions, and noise removal.

 

 

Further contributions to „7th OJA Forum“:

7th OJA Forum (Part 1) – From data to knowledge on skills

7th OJA Forum (Part 2) – ESCWA’s Sills Monitor

7th OJA Forum (Part 3) – Enhancing the Employability of Students: a LMI Model using OJA

7th OJA Forum (Part 4) – Profession classification in the messy real world

7th OJA Forum (Part 5) – A Hybrid Methodology for Job Ad Title Normalization

7th OJA Forum (Part 6) – Semantic Search with BERT

7th OJA Forum (Part 7) – Explainable AI in OJA Analysis: Unveiling Job Level Classifier