Structured meaning
Knowledge graphs and structured meaning
Knowledge graphs represent the world as entities and relationships. They help search engines, databases, libraries, businesses, and AI systems work with meaning as data.
Entities and relationships
A knowledge graph begins with entities: people, places, organizations, concepts, products, datasets, events, and works. It then records relationships among them. This graph form helps machines connect facts that may be scattered across many pages or systems.
The graph does not replace documents. It creates a structured layer over them. Documents provide explanation and context. Graphs provide identifiers, relationships, and queryable structure.
Schema.org and structured data
Schema.org gives publishers a shared vocabulary for describing pages, people, events, organizations, products, reviews, recipes, articles, datasets, and many other things. Google’s structured data guidance describes structured data as a standardized format for giving information about a page and classifying its content.
JSON-LD is a common way to place this structured data inside a web page. It lets publishers add machine-readable meaning while leaving the visible design unchanged.
Search and entity understanding
Search engines historically relied heavily on keywords and links. Knowledge graphs added entity-based understanding. A search system can recognize that a query refers to a known person, company, place, product, or concept, then connect that entity to related facts.
This is also why clean identifiers matter. When different sources use different names for the same thing, semantic systems need stable identifiers and disambiguation rules.
Internal links
Knowledge graphs build directly on ideas from the Semantic Web and public graph projects like Wikidata. Their current importance becomes clearer in AI and the New Semantic Layer.