Why Ontology Matters More Than Ever

Artificial intelligence (AI) can write reports, summarize meetings and answer questions with impressive confidence. What it cannot reliably do on its own is determine exactly what your organization means by every word it uses. When “partner” means five different things across three departments, even sophisticated AI has a problem. It may simply deliver that problem in beautifully polished prose.

This is why ontology becomes more important as AI use increases.

In information management, an ontology explicitly defines the concepts within a particular domain, their characteristics and their relationships. A taxonomy organizes information into categories; an ontology goes further by describing how things connect. A program serves an audience, receives funding, advances a goal and operates under particular policies. Those relationships give information usable meaning.

When an organization that uses “member,” “donor,” and “volunteer” throughout its records, one person might occupy all three roles, but those roles carry different expectations, permissions and histories. An ontology can distinguish the person from the roles and clarify how each relates to organizational activities. Without those distinctions, an AI system could retrieve relevant language while assembling a misleading picture.

The stakes grow when AI moves beyond drafting content into recommending decisions or initiating actions. An ambiguous definition that once caused an awkward meeting could influence thousands of automated responses. Greater speed makes shared meaning more consequential.

Ontologies also provide a foundation for connecting information across systems. Departments often describe the same things differently, while identical labels sometimes describe different things. Establishing explicit definitions and relationships gives developers a basis for mapping those differences. The argument is that this work becomes increasingly valuable as organizations ask AI to draw answers from multiple sources.

When incorporated into retrieval and validation processes, an ontology can help a system locate appropriate information and check whether certain relationships make sense. It does not automatically eliminate hallucinations. Generative AI can still produce false claims, a risk identified by the National Institute of Standards and Technology. A structured model of meaning must be paired with reliable sources, testing and human oversight.

There is also an accountability benefit. Explicit definitions give people something concrete to inspect and challenge. What counts as an active participant? Which activities qualify as service? Who decided? An ontology makes those assumptions available for discussion rather than leaving them buried in disconnected documents or individual interpretations.

Of course, an ontology can preserve outdated assumptions just as efficiently as useful ones. It requires stewardship: subject experts, clear ownership, regular review and room for disagreement. AI can assist with proposing concepts and connections, but people must decide whether those proposals accurately represent their work.

Organizations do not need to model everything before using AI. They can begin with a consequential question, define the concepts needed to answer it and expand as useful patterns emerge.

As AI becomes more capable, the responsibility to define meaning becomes more urgent. Before asking machines to connect the dots, we should agree on what the dots actually represent.

The future of AI depends on how content is prepared today. Access Innovations partners with organizations to turn metadata, semantics and structure into AI-ready infrastructure that protects meaning and enables confident innovation.

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Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, uniquely positioned to help you in your AI journey.