At its most basic, an ontology is a world model. It describes concepts that exist in the world and how they are related to one another. A taxonomy is a tree-like hierarchy that organizes concepts according to increasing levels of specificity. An Artificial Intelligence Ontology adds a second type of link between those concepts, explaining how they are related.
As a result, they can address the massive amounts of data used as input for machine learning training or output as results. Furthermore, ontology is appropriate for any organization's goal, which can be achieved through mathematical, logical, or semantic-based approaches. Essentially, while the concept of ontologies is simple, it has far-reaching implications. Hence these latest trends are used in neural sciences, education, and many other fields to make them better and efficient.
Artificial Intelligence Ontology can be used to make sense of the world. People subscribe to a very specific type of language-centric ontology, which isn't worth discussing here. Instead, the Semantic Web project (Tim Berners-Lee) is more likely to be of interest to you. The Semantic Web employs a type of description logic that is outside of my area of expertise. However, there are tools for processing this type of DL and gaining "understanding" from it. To work with this ontology, you should be familiar with the concept of Resource Description Framework triples.
The rapid advancement of artificial intelligence and its branches, such as machine learning and deep learning, which function on extracting relevant information and generating insights from data in order to find long-term and decisive solutions, is nothing new. Organizations, however, require data and code to run these algorithms. We need data science to turn this need into something meaningful.
