In computer science and artificial intelligence, an ontology is a formal, structured representation of knowledge within a specific domain. It defines the concepts, entities, properties, and relationships that exist in that domain, creating a shared vocabulary that both humans and machines can use to reason about information. Ontologies go beyond simple taxonomies by capturing complex relationships and rules governing how concepts relate to one another.
Ontologies are expressed using standardized languages such as OWL (Web Ontology Language) and RDF (Resource Description Framework), which are core technologies of the Semantic Web. Tools like Protege allow domain experts to build and edit ontologies visually. Well-known ontologies include Dublin Core for metadata, SNOMED CT for medical terminology, and Schema.org for structured web data.
The practical applications of ontologies are broad. In AI and natural language processing, ontologies enable knowledge graphs that power search engines, virtual assistants, and recommendation systems. In healthcare, they standardize medical vocabularies across systems. In enterprise settings, ontologies help with data integration across departments by providing a common framework for understanding disparate data sources. As AI systems get more sophisticated, ontologies give them a structured way to understand context and meaning.