Linked Data is a set of best practices for publishing and connecting structured data on the Web using standard technologies like URIs, HTTP, and the Resource Description Framework (RDF). It moves beyond a web of documents to a web of typed links between discrete, machine-readable entities, enabling automated agents to discover and reason over data from heterogeneous sources.
Glossary
Linked Data
A method of publishing structured data on the web so that it can be interlinked and become more useful through semantic queries, using standards like RDF and URIs.
SEMANTIC WEB STANDARD
What is Linked Data?
Linked Data is a method of publishing structured data on the web so that it can be interlinked and become more useful through semantic queries, using standards like RDF and URIs.
By using unique URIs as global identifiers for real-world things and returning RDF data upon dereferencing those URIs, Linked Data creates a decentralized, queryable knowledge graph. This foundational principle of the Semantic Web allows a dataset to explicitly define its relationship to external datasets, facilitating powerful cross-domain queries using languages like SPARQL.
Semantic Web Foundations
Key Features of Linked Data
Linked Data is a method of publishing structured data on the web so that it can be interlinked and become more useful through semantic queries. It relies on four core principles defined by Tim Berners-Lee.
01
Uniform Resource Identifiers (URIs)
Every entity, concept, or relationship in Linked Data is identified by a unique, globally scoped URI (Uniform Resource Identifier). Unlike traditional database keys, URIs are resolvable on the web, enabling decentralized naming. This ensures that two datasets referring to the same entity use the same identifier, eliminating ambiguity.
- URIs use the
http://scheme for dereferencability - Enables global, cross-domain entity disambiguation
- Foundational to Entity Linking and Entity Resolution
02
Resource Description Framework (RDF)
Linked Data is serialized using the RDF data model, which represents all information as atomic triples: a subject, predicate, and object. This graph-based structure is inherently schema-flexible and can merge data from any source without pre-defined table joins.
- A triple:
<Berlin> <isCapitalOf> <Germany> - Serialized in formats like Turtle, JSON-LD, or RDF/XML
- Stored and queried in a Triple Store
03
HTTP Dereferencability
URIs must be dereferenceable via standard HTTP protocols. When a client or crawler looks up a URI, the server returns useful, structured information about that resource using content negotiation. This turns the web into a giant, queryable database.
- A request to a person's URI returns their RDF data
- Uses HTTP 303 redirects to separate real-world things from documents
- Enables live traversal of the Semantic Web
04
Contextual Linking to Other Data
The ultimate value of Linked Data is realized when datasets connect to other external datasets. By linking out to authoritative URIs from sources like Wikidata or DBpedia, a dataset transitions from an isolated silo into a node in a global Knowledge Graph.
- Transforms isolated data into a federated Knowledge Graph
- Enables discovery of new facts through Link Prediction
- Powers multi-hop reasoning in Graph RAG architectures
05
SPARQL Query Language
Linked Data stored in RDF format is queried using SPARQL, a W3C standard. SPARQL allows for powerful graph pattern matching across distributed endpoints, enabling complex federated queries that join data from multiple sources without data warehousing.
- Syntax for matching triple patterns:
?subject ?predicate ?object - Supports federated queries with the
SERVICEkeyword - Fundamental to Knowledge Graph Question Answering (KGQA)
06
Ontologies and Vocabularies
Linked Data uses shared ontologies and controlled vocabularies to define the meaning of predicates and classes. Standards like RDFS and OWL provide a formal semantics that allows machines to infer new knowledge and validate data integrity.
- Schema.org is a widely adopted vocabulary for web content
- SHACL validates graph data against defined shapes
- Ontologies enable automated reasoning and Fact Verification
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Frequently Asked Questions
Precise answers to the most common technical questions about publishing and consuming structured, interlinked data on the web using RDF, URIs, and SPARQL.
Linked Data is a method of publishing structured data on the web so that it can be interlinked and become more useful through semantic queries. It works by using a uniform interface of HTTP URIs to identify entities, the Resource Description Framework (RDF) to represent data as subject-predicate-object triples, and SPARQL to query that data. When a URI is dereferenced, it returns RDF data describing the entity, which contains links to other URIs, creating a web of machine-readable facts. This transforms the web from a network of documents into a global, queryable database of interconnected entities, enabling AI systems to perform deterministic reasoning across disparate data sources without custom API integrations.
About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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