When AI needs to unite data consistently with other systems, known as interoperability, ontology standards are essential. Ontology standards are also needed in regulated sectors where explainable outputs provide the means for audits and other forms of compliance.
What are ontology standards?
Ontology standards refer to the established languages, frameworks and guidelines that are used to ground and constrain an AI agent, while enhancing context. Used to define the meanings of important concepts and how they relate to each other, ontologies in general can enable AI to reason with enterprise knowledge in a way that simple taxonomies or controlled vocabularies cannot.
Traditionally, ontology standards have also been used to avoid misunderstandings or mistakes among multilingual or multidisciplinary teams.
What are the main ontology standards?
The main ontology standards are OWL (Web Ontology Language), which is used to give data meaning, and RDF (Resource Description Framework), which is the data model. SKOS and Dublin Core are more basic ontology standards, used by libraries and archives for example to represents simple knowledge structures like taxonomies and thesauri.
SPARQL and SHACL are foundational W3C standards. While ontologies like OWL describe the overall structure and logic of a domain, SHACL is used to constrain how data is structured to ensure data quality and integrity. SPARQL meanwhile is the engine that queries RDF graphs, in much the same way that SQL is used to query relational databases.
In general, ontology standards help systems share data more reliably, and make it easier to reuse ontologies for different projects. They ensure that important words have the same meanings across different systems, tools, and domains, which is known as semantic consistency. Standards also help to make ontologies less onerous to scale and maintain.
What are the drawbacks of ontology standards?
On a technical level, ontology standards only make binary connections between objects. As a result they become unwieldy when handling polysemy, which is where a word has multiple related meanings depending on their context. This is a serious problem for more sophisticated AI, especially when enterprises want to introduce natural language querying for example.
On a human level, using ontology standards also requires domain experts to agree on the definitions of the things that matter for the task an AI is given. Reaching consensus on a so-called semantic layer can become very complex when apparently simple words like ‘customer’ mean subtly different things in different parts of the business or domain.
Like any other automated process, ontology governed AI also needs humans-in-the-loop (HITL) to monitor for semantic drift, maintain the system, and update knowledge where needed.
Is getting agreement on an ontology worth the effort?
Clearly some applications exist where occasional mistakes can be tolerated, and inference is not required. This means that a simple taxonomy or controlled vocabulary might be enough to do the job. However, without sufficient context, and the ability to make logical inferences, AI systems cease to be domain intelligent. In other words, without access to structured knowledge, AI ends up relying on statistical probability and pattern learning for the truth, which is essentially what large language models (LLMs) provide, with uncertain results.
Failing to get domain experts to reach agreement on definitions for an ontology is, in our experience, a significant bottleneck to the delivery of measurably valuable AI. Our own belief is that digital consensus-building methods, such as those provided by ontology-as-a-service companies (OaaS), are a necessary foundation for the automation of knowledge work at scale.
Why AI needs access to unwritten knowledge
Ultimately, human experts are the only source of the unwritten knowledge and know-how that often make an enterprise successful. That hard-to-define enterprise culture, sometimes referred to as tacit or implicit knowledge, largely sits in their heads, not in a database, making it hard or impossible for AI to access. Implicit knowledge covers everything from reasoning patterns, to debugging processes, and problem-solving strategies, as described in a 2026 paper by Hengyu Liu et al.
The success or failure of enterprise AI depends, therefore, on getting that implicit knowledge out of experts’ brains and into machine-readable form, under the control of the enterprise, so it can be executed reliably. Getting people to agree on the rules and concepts that make up an enterprise’s knowledge, by which implicit knowledge becomes explicit, is the only way to do that properly, in our view.