Ontology-as-a-service (OaaS) platforms are designed to help non-technical people structure domain knowledge so that it becomes machine-readable for AI services. OaaS platforms offer a supposedly user-friendly alternative to legacy ontology tools, and aim to be more accessible on price than enterprise-grade software.
What are ontologies?
Ontologies are frameworks that map and organise domain knowledge, defining the meanings of key concepts and how they relate to each other. In other words, they capture the ‘things that matter’ in the pursuit of an enterprise’s objectives.
By adopting ontology standards, ontologies bring together data from different systems (interoperability) in a way that’s consistent, which AI can process and interpret to gain better contextual understanding. This makes its outputs more useful and trustworthy, which are signs of high domain intelligence.
When integrated into operations, ontologies in our experience can reduce the risk of errors and hallucinations that arise when LLMs use unstructured or unverified data, for example. The process of anchoring LLMs to enterprise truth is known as grounding.
What makes ontology as a service different?
OaaS software competes with legacy ontology softwares by seeking to provide easier-to-use tools and methodologies at a relatively low cost, along with consensus building tools so that subject matter experts can agree on the definitions of concepts and relationships that form an ontology. Free ontology systems tend to be designed by, and for, academics or technical people. They require training to use, update and maintain. Paid-for versions, on the other hand, often come as part of a corporate consultancy package and are not necessarily appropriate for startups.
One of the first papers exploring the notion of OaaS for both large and small enterprises was a 2017 study by the RheinMain University of Applied Sciences entitled Ontology-Based Big Data Management.
Who uses ontology services?
Traditional ontology tools and apps are mostly aimed at insurance, automotive manufacturing and bioinformatics businesses, or have been developed for academic research purposes. In recent years, however, we have seen demand for trust layer infrastructure to work with AI agents spread beyond these core markets.
In particular, OaaS is designed to support small and medium-sized enterprises that use knowledge graphs, and domain experts who want to enable vertical AI services, but are worried about semantic drift.
What slows down ontology development?
The time and effort involved in mapping out and getting agreement on ontologies often slows the advance of intelligent systems in our experience. Stakeholders can be slow to contribute to ontology development. Subject matter experts sometimes disagree on the definitions of formal concepts, relationships and constraints for an ontology. Large stores of unstructured and paper-based data can also deter ontology development simply because it is not available for ingestion.
Without ontologies, however, AI services have less access to diverse data-sets for reasoning. Without contextual understanding, the outputs of AI are less useful and reliable.
How to build consensus around ontologies
Advanced OaaS provides online collaborative tools to ease the process of discussion, negotiation and consensus-building around an ontology or AI trust layer. OaaS platforms offer features that track progress, engage the wider domain community, and introduce deadlines to keep up momentum – as well as enabling modelling and explainability.
Our own OaaS platform can also automate aspects of ontology-building, and provide useful methodologies, such as the Delphi Method, to get a working ontology over the line. A good starting-point for building an ontology is to ask experts our three key questions about their domain.