The Semantic Web: RDF, Linked Data, Knowledge Graphs & Machine-Readable Data
The Semantic Web is a standards-based way to publish data with explicit identifiers, types, and relationships so different systems can combine it without guessing what each field means. Its practical core is RDF for statements, shared vocabularies for meaning, SPARQL for graph queries, and stable HTTP identifiers—not a promise that the web will reason automatically.
RDF models facts as identified relationships
RDF represents a statement as subject, predicate, and object. Each resource and relationship should use a stable IRI; literals carry datatype or language. The W3C RDF 1.1 Primer explains the model and serializations such as Turtle and JSON-LD. A small example is inspectable without a specialized database:
@prefix schema: <https://schema.org/> .
<https://example.com/articles/search>
a schema:TechArticle ;
schema:name "Search architecture"@en ;
schema:dateModified "2026-07-28" .The value is the stable identity and vocabulary, not the syntax. JSON with undocumented local keys remains ambiguous even if it is converted mechanically to RDF.
Vocabularies add constraints and shared meaning
RDFS supplies class and property relationships; OWL adds more expressive ontology constructs. The OWL 2 overview describes several profiles with different reasoning costs. Use the least expressive model that answers the integration need. Closed-world validation requirements belong in a validation layer such as SHACL rather than being assumed from open-world ontology statements.
Reuse established vocabularies where they fit, but document local extensions and ownership. Ontologies fail when teams create abstract class hierarchies without stable source data, governance, or a query that benefits.
SPARQL queries graphs across sources
SPARQL matches graph patterns and can join relationships that would require several relational tables or API calls. The W3C SPARQL 1.1 Query specification defines filtering, aggregation, optional patterns, and federated queries. Public endpoints need timeouts, result limits, and query-cost controls; arbitrary graph patterns can be expensive.
Use linked data where interoperability pays
Good candidates include catalogs shared between institutions, research metadata, regulatory identifiers, and knowledge graphs joining independently governed sources. Ordinary application state may remain simpler in a relational database. Keep source provenance, update timestamps, and conflict policy: combining two graphs does not make contradictory facts disappear.
Search can use graph entities for disambiguation, filters, and enrichment, while embeddings handle fuzzy language. Neither replaces the other. Test URI persistence, vocabulary versioning, deleted entities, provenance loss, and reasoning cost before making a graph part of a critical request path.
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