The importance of AI-based Skill Taxonomies

Among other things, a skill taxonomy makes it possible to describe the skills required per job.

O*Net or ESCO are well-known skill taxonomies that represent a standard and are available free of charge. However, they are based on outdated technology. On the other hand, modern AI-based approaches enable an efficient, more updated and simpler way of determining the skills per job.

AI-based Competence Taxonomies

Providers of AI-based skill taxonomies collect information about jobs from sources such as LinkedIn, job portals or other relevant sources. Next, so-called NLP models (linguistic data processing models) help to identify which skills can be found in which jobs. These analyses are not entirely simple, because the primary goal is to identify the skills that are of great importance for the job. Recognising the relevance of a skill (importance) and its level (professionalism) is crucial. If these analyses are carried out on a daily basis, the job-skill relations is checked daily and dynamically adjusted to market changes.

If you want to ensure the relevant skills within a company for the future, you have to rely on this market view. Thanks to the job-skill relation, potential candidates can be matched with job offers, internal candidates can be found for projects, the skill gap between a person and a targeted job can be identified and career paths between jobs with similar skills can be found.

At Learning Pool, we have developed a multilingual and unique skills taxonomy that includes job titles, industries, sectors, company names and other important attributes in addition to skills and their professionalism levels.

Click here to read the full article “The Rise of Skills Taxonomies” by David Creelman.