Domain intelligence (DI) refers to an AI model’s ability to reason over a defined area of knowledge in a way that is both accurate and useful. High domain intelligence often relies on knowledge being structured in a machine-readable format using ontologies and knowledge graphs.
Critical domains such as healthcare and defence need high domain intelligence, as do intelligent systems where actions and decisions are delegated to vertical AI agents.
How does an ontology help domain intelligence?
The knowledge frameworks that structure a domain’s information, rules and guidelines for machine-reading are called ontologies. Guided by constraints encoded within an ontology, intelligent systems can execute workflows and problem-solving strategies with less chance of hallucinations and errors, thereby raising their DI.
In many DI domains, ontology standards are needed to increase semantic precision, and enhance interoperability whereby data can be exchanged smoothly between systems. A 2024 study by BioRxiv tied domain intelligence to ontology-driven knowledge representation in biomedicine and healthcare.
What are emerging trends in DI?
A drawback of structured knowledge is that it can be relatively inflexible, whereas unstructured data can be faster and easier to scale. The risk however is that unstructured data can expose large language models (LLMs) to errors and hallucinations – which are signs of low DI.
New approaches such as neuro-symbolic AI are trying to combine the virtues of neural networks, which specialise in handling vast amounts of unstructured data, with the rigour of the more structured semantic web, to address these issues.
Which sectors need high DI?
Healthcare, law and finance are governed by complex compliance requirements, ethical considerations, and professional standards – with very low tolerance for errors. This means they need high DI to obtain significant value from an intelligent system.
These domains have specialised language that needs to be captured as controlled vocabulary for the AI to reason with accurately, along with rules defining the relationships between each concept. The strong contextual understanding that is needed for these systems to become valuable, can be enhanced by using techniques such as hybrid RAG.
In complex domains, all of this semantic detail needs to be modelled and interpreted by a knowledge engineer before a domain intelligent system can be tested and eventually put to work. The outputs of the AI also need to be explainable (XAI), especially for compliance purposes.
How does domain intelligence help brands?
Demand for domain intelligent systems is growing across a range of data-heavy sectors that are pushing ahead with digitalisation, including consumer brands. In these fields vertical AI agents aim to provide 24 hour support consistently across different languages and cultures. However, the ability to handle nuanced queries and edge cases as effectively as humans is an ongoing challenge.
What is Ontology as a Service (OaaS)?
Ontology software platforms are designed to help builders of domain intelligent systems by offering an easier, faster and more flexible development environment. Ontology-as-a-service (OaaS) aims to extend these tools to non-experts and smaller businesses in particular so they can build, query, and integrate ontologies.
These platforms often include collaborative tools, version control, and governance features. They can also help with building consensus around ontology standards using techniques such as the Delphi Method.