Data-Modelling

An ontology gives a knowledge graph an explicit, machine-readable account of a domain: the concepts that exist, the relationships between them, and the statements that follow from those definitions. It can align data from different systems, support inference, and make the intended meaning of a graph inspectable. That does not make an ontology a database schema, a data-quality checker, or a guarantee that every statement in the graph is true. Those responsibilities require different mechanisms. In particular, OWL reasoning and SHACL validation answer different questions: ...

A knowledge graph represents entities and the relationships between them in a graph-shaped data model. Its value is not that graphs automatically contain truth or make systems explainable. The value is that identity, relationships, provenance, and domain meaning can be made explicit and queried together. That distinction matters in applied AI. A graph built from noisy documents can preserve extraction errors. A graph query can return an irrelevant path. An ontology can support valid inference from false assertions. A language model can still misread correctly retrieved facts. Knowledge graphs create useful structure; reliability comes from the evidence, controls, and evaluation around that structure. ...