Two words appear on nearly every page of this site. This page explains them once, in plain language, and adds no further jargon.
An ontology is an agreement made in advance: what kinds of things this organisation keeps, what each kind is called, and how the kinds connect to one another.
Say the pattern has objects, and it has makers. Every object goes by a name. Every maker works out of a place. And an object joins a maker through one phrase — made by. Nothing real has been filed against any of it yet.
Written once. Used across the whole organisation.
Put what you actually hold into the pattern and you have a knowledge graph. One hammered alms bowl, made by the smiths of Ban Bu. The bowl carries its own name, the smiths carry their own place, and every line drawn in the pattern now has something real hanging off it.
Here is the difference that earns its keep. In ordinary systems the link between the bowl and the smiths gets rebuilt from scratch every time somebody asks for it. In a graph that link is already there — it has a name, it has a direction, and anything can walk along it.
Two museums can hold the same kinds of objects. But if one exists to teach and the other exists to research, the lines worth drawing are different.
An off-the-shelf ontology therefore serves neither. We design from the organisation's mission, not from an industry template.
A general AI guesses from the shape of sentences. An AI working on your knowledge graph follows the lines your own people drew.
So answers trace back to evidence, the same question returns the same answer, and nothing is claimed beyond what the organisation stands behind.
An ontology that matches the mission is what lets AI work for the organisation, rather than in place of it.
Which makes the hard part of this work not technical. It is getting departments to agree on what things are — and only your own people can do that.
90 minutes. Bring your curators or librarians, and one question your institution couldn't answer.
Or write directly — hello@neogens.co