Structuring knowledge with concepts, properties, and relations.
While logic provides a formal way to express rules and facts, ontologies and semantic networks offer a structured, intuitive way to represent knowledge about a domain. They model the world as a network of concepts (nodes) and relationships (edges). A semantic network is a graph-based knowledge representation where nodes represent objects or concepts, and links represent the relationships between them. For example, a node 'Canary' might be connected to a node 'Bird' with an 'is-a' link. The 'Bird' node might then be connected to a 'can_fly' property node. This structure supports a type of reasoning called 'inheritance.' Because a canary 'is-a' bird, it inherits the properties of a bird, such as the ability to fly. This makes the representation efficient, as common properties don't need to be stated for every single instance. An ontology is a more formal and rigorous version of a semantic network. It explicitly defines a set of concepts and categories in a subject area or domain, along with their properties and the relationships between them. Ontologies go beyond the simple 'is-a' and property links of semantic networks to include more complex constraints, such as cardinality (e.g., 'a person has exactly two biological parents') and disjointness (e.g., 'a cat cannot be a dog'). They are crucial for creating shared, reusable knowledge bases that enable interoperability between different AI systems. The Semantic Web, for example, relies on ontologies (like RDF and OWL) to allow machines to understand the content of the web, enabling more intelligent search and data integration.