The taxonomies are designed to be:
Professions are available in 10 languages, skills in 6.
The taxonomies contain job titles and skills from across the job market.
The taxonomies continuously undergo improvements based on market feedback.
The profession and skills taxonomies are connected to each other through weighted links that represent the degree of association between professions and skills.
Understand job titles by mapping them to professions from the taxonomy, as well as industry standards such as O*NET and ISCO. The input to the Professions API can be a vacancy title, or a user-typed input in the context of autocomplete.
Understand skill data by extracting and standardizing skills from any text with the Skills API, be it cover letters, training descriptions, or performance reviews. The input can also be user-typed input or a skill description.
Understand relations between professions and skills
The Ontology API makes it possible to infer relevant skills given a profession, or fitting professions given a skill set. It can also reveal the ‘skill gap’ between any pair of professions.
Data Enrichment APIs: Helping you better understand skills and professions
Textkernel’s profession and skill taxonomies are at the heart of the Data Enrichment APIs. These APIs can be leveraged to Professions API, Skills, Ontology API.
Data standardization for matching
How does the standardization of profession and skill data contribute to matching people and jobs? Let’s look at an example:
On the left we see a situation where matching happens merely on the basis of textual overlap. Because the words do not match exactly, perfectly suitable candidates would be overlooked. If taxonomy-based standardization is added to the process, as on the right, the job titles and skills mentioned in the documents are recognized as identical. Thus, data enrichment helps unlock the potential of your talent data.
Connecting roles to roles
In addition to matching specific profiles and job listings, the Ontology API makes it possible to understand how roles relate to each other in terms of the skills they require. Given a current and subsequent role, it indicates which skills are “transferable”, and which are likely to require upskilling activities.
Reliable analytics also requires standardization of data. To illustrate this, let’s look at a scenario with and without standardization of job titles.
In the scenario on the left, we’re essentially counting words, not jobs. Without accounting for synonyms and variation in wording’, the results of analyses like these are not actionable. The Professions API and Skills API help achieve this: they represent your data in a way that goes beyond words: in terms of the units (professions, skills) that are actually of interest.
The Data Enrichment APIs can help you to:
Extract and normalize skills from any HR-related text to pre-populate skill profiles.
Extract skills from training descriptions, and leverage those for targeted upskilling recommendations.
Improve analytics by standardizing skill and job data to Textkernel's comprehensive, cross-lingual taxonomies.
Import the market’s most comprehensive taxonomies of skills and professions in your organization's tools and processes.
Create smart matching solutions by leveraging Textkernel's extensive knowledge base on synonyms and profession-skill relations.
Automatically enrich job architectures and job catalogues with suggestions of relevant skills.
Generate skill-based recommendations of (non-obvious) job transitions.
Understand the skill gap between two professions, or between a current skill set and an aspired next role.