Ontology-as-a-service (OaaS) platforms are designed to help non-technical people structure domain knowledge in a way that AI systems can process for better performance. OaaS platforms offer a supposedly user-friendly alternative to legacy ontology tools, and aim to be more accessible on price than enterprise-grade systems.
What are ontologies?
Ontologies are frameworks that map and organise domain knowledge, standardising the meanings of key words. They bring together data from different systems 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.
Vertical AI teams, businesses and domain experts turn to ontologies to reduce the risk of errors and hallucinations that arise when LLMs use unstructured data. OaaS platforms try to speed up the process of designing, agreeing and implementing ontologies.
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. 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, demand for trustworthy AI services has spread beyond these core markets.
In particular, OaaS is designed to support small and medium-sized enterprises and domain experts who want to enable vertical AI services, but lack deep technical infrastructure.
What slows down ontology development?
The time and effort involved in mapping out and getting agreement on ontologies often slows the advance of domain intelligent systems. Stakeholders can be slow to contribute to ontology development. Subject matter experts sometimes disagree on the definitions of formal concepts, relationships and rules for an ontology.
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. OaaS platforms offer features that track progress, model knowledge frameworks, engage the wider domain community, and introduce deadlines to keep up momentum. OaaS can also automate aspects of ontology-building, and provide useful methodologies, such as the Delphi Method, to get a working ontology over the line.