Building a knowledge graph without an ontology can work as a shortcut during the prototype phase of a project, when flexibility is prioritised over accuracy and reliability. But as soon as you need trustworthy and user-friendly querying of domain knowledge, more efficient data integration, and AI agents that can usefully reason – it might be time to consider an ontology.
What is a knowledge graph?
A knowledge graph is a connected network used to store and retrieve factual data, often from multiple sources. Knowledge graphs can be built for specific domains but they also underpin search engines such as Google.
A knowledge graph’s focus on how specific real-world things or people (known as instances) relate to one another, makes it well-suited to answering everyday queries using contextual understanding. What they are less good at is ensuring rigour, high accuracy and the ability to share data between domains, known as interoperability – which is what ontologies can deliver.
What is an ontology?
An ontology is a structured framework for domain knowledge that is applied to the knowledge graph using mechanisms such as constraints, taxonomies, and semantic relationships, often encoded in standard ontology formats like OWL or RDF. In other words, the ontology provides clear rules and boundaries for the knowledge graph to do its job.
How does an ontology benefit a knowledge graph?
Introducing an ontology using a hybrid approach can retain the flexibility of a knowledge graph while adding the benefits of a reliable high-level structure. For example, in a medical domain a knowledge graph might enable the AI agent to query patient data efficiently, but with an ontology it could also infer diagnoses, thereby improving the system’s domain intelligence.
How does an ontology benefit an AI agent?
An ontology makes the AI’s outputs more reliable and trustworthy by restricting the agent to verified domain data, and obliging it to follow the domain’s agreed rules and standards. Properly deployed, it can also make the system perform better.
A 2024 study found that ontologies benefit AI models when handling dynamic, unforeseen situations. The ontology-enhanced model demonstrated improved performance compared to traditional reasoning and machine learning methods, which included reinforcement learning.
Which sectors use ontologies the most?
Legacy ontology software was typically used by enterprise-level bioinformatics, advanced manufacturing and insurance companies, or research institutions. This is because ontology development requires consensus around its design, which can be a time-consuming and costly process.
In the age of AI, however, a wider range of sectors that use knowledge graphs are showing interest in ontologies so they can develop intelligent systems that are accurate and trustworthy. In response, ontology-as-a-service (OaaS) platforms have begun to offer non-technical people a means of automating their systems, and hopefully obtaining scale without the pain of major re-factoring.