Ontology & Knowledge Representation

A business ontology is a formal model of the concepts specific to how a company creates value — capabilities, strategies, operating models, outcomes, and their relationships — giving the organisation a precise, shared language for reasoning about strategy and execution.

A controlled vocabulary is a curated, agreed set of terms used to describe and organise information consistently, removing the ambiguity of free-text labels by giving each concept one preferred name and defining how synonyms and variants map to it.

A domain model is a structured representation of the key concepts, entities, and rules within a specific area of a business, capturing how those concepts relate so that teams and software share a precise, common understanding of the problem space they are working in.

An enterprise data model is an organisation-wide blueprint of the data it holds — the core entities, their attributes, and the relationships between them — providing a consistent structure that spans systems so that data means the same thing wherever it is stored or used.

An enterprise knowledge model is a structured, governed representation of an organisation's strategic and operational knowledge — capabilities, methods, entities, and their relationships — that both people and AI systems can query to ground decisions in a shared, traceable source of meaning.

An enterprise ontology is a formal, machine-readable model of the concepts, entities, and relationships that define how an organisation operates, giving people and software a single shared vocabulary for strategy, capabilities, processes, data, and the connections between them.

A knowledge graph is a network of real-world entities — such as capabilities, processes, or outcomes — connected by defined relationships, stored so that both people and machines can navigate the connections and reason across them rather than reading isolated records.

Knowledge representation is the field within artificial intelligence concerned with encoding facts, concepts, and relationships in a structured form that a computer can store, query, and reason over, so that machines can draw conclusions rather than only retrieve stored text.

In business and AI, an ontology is a formal specification of a domain's concepts, categories, and relationships, written so that software can interpret meaning consistently — providing the shared vocabulary that lets AI systems reason over enterprise knowledge instead of guessing from patterns alone.

A semantic layer is a business-friendly abstraction that sits between raw data systems and the people or applications using them, translating technical fields into consistent, defined business concepts so that everyone queries the same meaning regardless of where the data lives.

Semantic metadata is descriptive information attached to content or data that captures its meaning and its relationships to defined concepts, rather than only its format — allowing machines to understand what a piece of information is about and how it connects to everything else.

A single source of truth is a practice of structuring information so that every data element is stored and maintained in exactly one authoritative place, ensuring that everyone and every system references the same current version rather than working from divergent, conflicting copies.

Taxonomy vs ontology describes the difference between two ways of organising knowledge: a taxonomy is a hierarchy that classifies things into nested categories, while an ontology is a richer model that also defines many types of relationship between things, enabling machine reasoning rather than only classification.