How I applied taxonomy to Digital Job Recruitment

I have been working within the HR tech space for more than eight years, and I quickly discovered that job descriptions contain a lot of valuable information for recruiters, labor market analysts and economists. To group these jobs together and be able to aggregate the insights, it was essential that jobs would be classified to an occupational taxonomy. 

But what is meant by taxonomy?

In a general sense, when you structure raw data, you can then extract insights from that data. We live in a world where availability of data will continue to grow in exponential ways. We can make better use of that data by classifying it in a clear and well-balanced structure.

An every-day example of taxonomy is the way books are organized in a bookstore. First, at the highest level you have the fiction and non-fiction categories. Under the fiction category, you have genres such as legend, comics, sci-fi, mystery and many other genres. Under the non-fiction category, you typically find biography, memoir, reference, textbook and other similar genres. Within each of these genres, books then belong to a subgenre. For example, under sci-fi, there is alien invasion, post-apocalyptic, alternative universe, etc. This taxonomy classification makes it easy for customers to walk into a bookstore and find what they are looking for more easily. 

Looking at the occupation taxonomy standards that were available, I could not find one that was granular enough to ensure that we could get to actionable insights. So, I decided that we had to build our own granular occupational taxonomy.

So, when talking about taxonomy for Search Engines, we mean a conceptual hierarchical structure where keywords are arranged in clusters and then correlated with connected macro-topics.

I took advantage of the taxonomy concept tree and I used Artificial Intelligence and Machine Learning to apply it to the entire cycle of digital job recruitment, from research to campaigning and the talents’ acquisition phase. 

That’s how Smart Intuition Technology™ was born.

This hierarchy, which includes every job and industry sector, is based on the internationally recognized O*NET-SOC classification and contains 6 levels of categories which provide more than 10,000 detailed occupational classifications based on education, seniority, skills and tasks.

Jobrapido has built an AI driven technology that uses millions of jobs collected on its platform to “educate” the deep learning algorithm; that is, to recognize categories and classifications of the hierarchy.

Thanks to the use of Smart Intuition Technology™, Jobrapido can now identify attributes related to these categories and create applications such as matching and campaigning using the power of taxonomy.

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