Taxonomies, Ontologies and Machine Learning: The Future of Knowledge Management

Taxonomies, Ontologies and Machine Learning: The Future of Knowledge Management

Kurt Cagle Contributor
COGNITIVE WORLD Contributor Group

As an ontologist, I’m often asked about the distinctions between taxonomies and ontologies, and whether ontologies are replacing taxonomies. The second question is easy to answer: “No.” Both taxonomies and ontologies serve vital, and often complementary, roles … if they are used right.

A taxonomy is, to put it simply, a categorization scheme. Most readers should be familiar with a few critical taxonomies such as the Linnaeus Taxonomy used to represent how animals are related to one another, and the Dewey Decimal System for libraries, which represents subject areas of interest. Others are more subtle. For instance, the Pantone Color System (PCS) is a commercial system used to identify specific swatches of color with a name and a code. Military organizations have ranks with names and designations that indicate not only experience but also authority, such as a Colonel (O6 in the US Army or Air Force) or a Chief Petty Officer (E7 in the US Navy or Coast Guard).

Taxonomies, in this case, identify specific names, definitions and code designations, but often also have a (usually implied) ordering system as well. Frequently, such ordering is rubric (or subject matter) oriented, such that everything is contained within a hierarchy, with the hierarchy becoming more specialized as you move toward leaves of the hierarchy, and more generalized as you move towards the root. Hierarchies are also contextually inclusive – if you identify a given resource as being associated with a term in a hierarchy, this also implies that the resource is part of the broader categories (e.g., if my cat “Bright Eyes” is identified as being a cat, it is also considered to being of the cat family, a mammal, a chordate (it has a backbone) and an animal respectively.

Most taxonomies attempt to ensure that for any given resource, there is one and only one bucket (classification), that a given entity can fit into in that ontology at a leaf level. A cat, for instance, cannot also be a dog. That does not mean that at a more general level both don’t share a common rubric. Cats and dogs, for instance, are both carnivores in the Linnaean System, which hints that categorization is usually not quite as cut and dried as it would seem to be at first glance. Indeed, that particular system actually has different strata (ranks) of comparison, and as such represents several different but interrelated classification vocabularies. Both cat and dog (or felis domesticus and canis familiaris, respectively) are Species, while Carnivora, to which they both belong is an Order. The idea that the same resource can have two categories apply to it holds because of a set of relationships:

https://www.forbes.com/sites/cognitiveworld/2019/03/12/taxonomies-ontologies-and-machine-learning-the-future-of-knowledge-management/#2ec968576e85