Would you buy a wheel if someone told you it was a bicycle?

After recently stumbling upon this Forbes post from 2019, and with skills ontologies entering the Gartner HCM Tech hype cycle, we decided it’s high time to discuss the difference between taxonomies and ontologies again. Although we have been developing and explaining our ontology for over 10 years, many HR and labor market professionals still let themselves be sold on the idea that a taxonomy is good enough for jobs and skills matching. Now, finally, after trying out one disappointing solution after the other, the idea is slowly catching on that they are being duped. And as ontologies start to trend, many providers are beginning to use this “more fashionable” term. But do not be fooled: their product hasn’t changed. It is still just a taxonomy. Speaking from experience, it takes years to build a true ontology. Why care? Because the difference in performance is massive.

As a reminder, a taxonomy is a hierarchical structure to classify information, i.e., the only possible relation between concepts in a taxonomy is of type “is a”. Think Yellow Pages or animal taxonomies. An ontology is a framework that describes information by establishing the properties and relationships between the concepts. Now if we want a machine to perform a task like job matching for us, we need to share our contextual knowledge with the machine. Meaning that we need to find a way to represent our knowledge in a machine-readable way. Given any specific domain, say, jobs and skills, which do you think could represent human knowledge better, a taxonomy or an ontology?

When we think about things or concepts, we automatically associate them with other things or concepts. Based on our knowledge, we make connections and set up a context. We think of a bicycle and know that it is made up of components (wheels, handlebars, seat, etc.), and is a vehicle. But we also know that some people can ride a bicycle, that this skill has to be learnt, that bicycles can go on roads or across fields and that cycling is good exercise. It’s more ecofriendly than a car, doesn’t need fuel, and so on and so forth. We can reason that in terms of use, a bicycle is similar to a tricycle for small children, but not for adults. We may know that in some countries, we are required to wear a helmet when cycling. Our knowledge about bicycles is not hierarchical, the concepts we can connect it with do not just fit into a cascade of “is a” relations, but instead satisfy relations like “has a”, “can”, “is similar to”, “requires”, etc. By definition, this knowledge cannot be represented by a taxonomy. But it can be represented in an ontology.

The same is true when we think about jobs and skills. Even with little knowledge of medical care, we know that ICU nurses have skills in common with psychiatric nurses, but that there are also must-have skills for ICU nurses that psychiatric nurses do not need, and vice versa. So, we can draw on common or contextual knowledge to determine that these two occupations are similar, but probably not similar enough for a good job-candidate match. We can infer other important information as well. For example, that a registered nurse requires official certification. Or that a head nurse will need additional skills like leadership.

We also know intuitively that the skills and tasks of a software test engineer have nothing to do with those of a nurse, even if the software company description states that they provide solutions that help nurses (amongst others). But without an ontology, this will show up in matching results.


Would you buy a wheel if someone told you it was a bicycle?


To truly capture the complexity of this domain of jobs and skills, together with its many interdependencies, such as similarities and differences between various specialties, inferred skills, knowledge of various computer applications, or required certifications or training, there is simply no way around an extensive ontology that describes all these various aspects of job and skills-related data. So next time someone tries to sell you an ontology, make sure you’re not just getting a souped-up taxonomy.

If you’re interested in a more in-depth explanation of the difference between taxonomies and ontologies, read the Forbes post for the general concepts, or this JANZZ post for a discussion in the context of matching. Or you can experience the difference by comparing your current matching solution against ours. Contact us at info@janzz.technology.