What is an Ontology?

Philosophically, an ontology is the study of being, existence, and/or reality that is designed to investigate not only what entities exist but how they can be categorized.

In computer science and, more specifically, the data age, an ontology is a formal, machine readable, specification of entities, their properties, and their relationships within a domain that is used to structure information in a way that systems can share and structure it.

In the early days of semantic technology, an ontology was used to structure data in a meaningful way to allow sophisticated models to process, and make sense of, natural language with relatively high degrees of accuracy. It was usually expressed in a formal ontology language that allowed for detailed entity, relationship, part of speech, and even concept definitions. They were often defined in such a way they could be organized into interconnected libraries which formally organized knowledge into large, connected, corpuses that could be deterministically processed (hallucination free) and completely understood by any application that was capable of processing the language the ontologies in the library were encoded in.

And this was the true beginning of the semantic web, which was also known as Web 3.0, which was still in its infancy in the 2010s, but starting to take off by early (early) adopters (with almost 2% of web domains containing semantic markup circa 2014).

But then five things happened.

1. SaaS exploded, and so did the need for data, and the ability to consume it in standard formats.

2. GPT-1 was released in 2018 and the Gen-AI craze began shortly thereafter, leading us down the hallucinatory hole of incessant inanity that every consultant thought could power everything.

3. This led to the agentic craze, which increased the demand for data (and the desire to consume it in structured formats).

4. Every SaaS provider, and their dog all of a sudden needed multiple, steady, streams of data in standard formats to power their agentic applications.

5. In response, every data provider responded by adopting a simple data standard, calling it an ontology, even if all they were serving up was average scope 3 carbon data by country and factory type.

And now the term has no meaning since it’s the term used by every SaaS vendor and data supplier to essentially describe their data file structure. No formality. No relationships. No underlying structure that allows the machine to actually reason. Just another random data file blended into the data soup that feeds the hallucinatory engine that will tell us to go over the cliff like lemmings (and lead countless to their deaths as they cognitively surrender to what the AI tells them to do).

What could have been our saving grace (if Web 3.0 research had continued and true ontologies of ontologies had been created) might soon be the source of our demise as Gen-AI blends together mismatched data with flawed reasoning and produces the digital equivalent of toxic waste.