People use the word ontology to mean different things, e.g. glossaries & data dictionaries, thesauri & taxonomies, schemas & data models, and formal ontologies & inference. A formal ontology is a controlled vocabulary expressed in an ontology representation language. This language has a grammar for using vocabulary terms to express something meaningful within a specified domain of interest. The grammar contains formal constraints (e.g., specifies what it means to be a well-formed statement, assertion, query, etc.) on how terms in the ontology’s controlled vocabulary can be used together.

People make commitments to use a specific controlled vocabulary or ontology for a domain of interest. Enforcement of an ontology's grammar may be rigorous or lax. Frequently, the grammar for a "light-weight" ontology is not complet ely specified, i.e., it has implicit rules t hat are not explicit ly documented.

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The word 'ontology' has been used to refer to all of the above things [=controlled vocabulary, taxonomy and thesaurus]. When used in the AI/Knowledge Representation community, it tends to refer to things that have a rich and formal logic-based language for specifying meaning of the terms. Both a thesaurus and a taxonomy can be seen as having a simple language that could be given a grammar, although this is not normally done. Usually they are not formal, in the sense that there is no formal semantics given for the language. However, one can create a model in UML and a model in some formal ontology language and they can have identical meaning. It is thus not useful to say one is an ontology and the other is not because one lacks a formal semantics. The truth is there is a fuzzy line connecting these things.