Making Sense of Skills: Neural Network Models for Skills Semantics

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With skills now front and center in every company’s talent initiatives, our resident AI expert Rabih Zbib decided to offer some valuable insights into how machine learning and AI can offer valuable aid in the form of skills semantics.

Rabih currently works at Avature as Director of Natural Language Processing & Machine Learning. He holds a PhD and a Masters in Science from MIT and is constantly working on improving talent strategies through the AI lens.

The world of talent management is going through a tectonic change, one in which skills play a central role in how HR teams acquire and manage their talent. As this process has accelerated, the need for automatic extraction and measurement of skills when sourcing candidates has also increased significantly.  So much so that skills have been dubbed the “new currency of talent”.

While we have already described Avature’s approach to skills management and AI’s role in a previous article,  in this one, we will focus on one of its key aspects: skills semantics. In other words, understanding the meaning of skills.

Why Are Skills Semantics Important?

If skills are used as an indicator for specific knowledge or expertise needed to achieve an outcome, then

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