Ontologies bring rules and constraints to the machine-readable, interconnected knowledge of the semantic web. With an ontology to formally define what concepts mean and how they relate to one another, AI agents can reason better, interact more effectively with humans, and be entrusted with more important tasks.
What is the semantic web?
The semantic web brings clearly defined meaning to information on the worldwide web so that it can be interpreted by machines. It uses controlled vocabularies and ontologies that help systems to integrate their data, especially in research-heavy sectors such as medicine and advanced engineering, while enabling querying and reasoning across systems.
Underpinning the semantic web are ontology standards like RDF, OWL and SPARQL, which developers use to structure data as interconnected knowledge graphs.
How do ontologies support the semantic web?
The semantic web grows by linking data across different sources. Ontologies provide a reusable set of concepts, relationships, and rules so that these sources can be united in a consistent way (interoperability)
Without ontologies, ‘car’ on one website and ‘automobile’ on another are disconnected. With ontologies, both can be understood as instances of a single concept, such as ‘vehicle’. This alignment of shared meanings (synonyms) makes integration possible, providing AI agents with greater contextual understanding that raises their domain intelligence.
A 2025 study provides a useful survey of how ontologies are embedded in vector spaces, combining symbolic ontology structure with machine learning.
How do ontologies enable AI reasoning?
Ontologies allow new knowledge to be inferred from existing facts. In other words, the ontology enables AI to draw its own conclusions from data, based on the rules and constraints of the ontology that governs it.
For example, if an ontology encodes that all cardiologists are doctors, and a dataset asserts that Alice is a cardiologist, then a reasoner can infer that Alice is a doctor. Obviously, the quality of the reasoning depends on the data the AI agent is using, and whether the ontology design is fit for purpose.
What are the drawbacks of ontologies?
Ontologies can be inflexible and difficult to scale unless they are carefully scoped and designed. They also need to be regularly maintained and updated to accommodate new knowledge. Time-consuming consensus building methods are often needed to obtain agreement among domain experts and owners on issues such as the definition of concepts within the ontology. Ontologies can also struggle with polysemy, where a single word can have many different but related meanings depending on their context.
Ontology-as-a-service (OaaS) platforms are emerging that make the process of designing, implementing and maintaining an ontology easier and swifter for non-technical people. For large and complex builds, however, an ontological engineer may still be needed.