What ontologies and knowledge graphs are
An ontology defines the concepts in a domain and the relationships or constraints that connect them. A knowledge graph applies that model to real entities and facts, creating a structure that software can traverse, validate, and reason over.
When you actually need them
They are useful when terminology varies across teams, relationships carry important meaning, or decisions depend on more than text similarity. Examples include product taxonomies, eligibility rules, organizational knowledge, research networks, and complex document domains.
How they differ from adjacent solutions
A vector store finds semantically similar content but does not necessarily know what a relationship means. A semantic layer governs business data and metrics. An ontology can support either, while a knowledge graph becomes valuable when connected entities and explicit rules are central to the use case.
How I build them
I begin with the questions the model must answer, then define a minimal vocabulary, identifiers, relationships, provenance, and validation rules. The graph is connected to retrieval or operational workflows only after its ownership and update process are clear.
Common mistakes
The main failure is modeling the whole organization before proving one valuable query or decision. Other problems include duplicate identities, unclear provenance, uncontrolled vocabulary growth, and a graph that is technically rich but disconnected from the product using it.