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Fundamentals of Semantic Web

Fundamentals of Semantic Web: RDF, OWL, and SPARQL enable machine-readable data, linked open data, and intelligent web inference.

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Course Detail

Overview: Introduces Semantic Web standards for machine-readable data on the web.

Course Features

  • Week 1-2: Semantic Web vision
  • RDFS/OWL ontologies, reasoning 
  • SPARQL queries

Chakir Mahjoubi

Instructor in Semantic Web

Semantic Web instructors are PhD experts in knowledge engineering, with hands-on experience in RDF/OWL, SPARQL, and ontology tools like Protégé..

  • 20 hours on-demand video
  • Full Lifetime Access
  • Access on Mobile and TV
  • Certificate of Completion

Course Content

11 sections • 28 lecture • 19h 33m total length

Tim Berners-Lee envisioned the Semantic Web as a “web of data” where machines understand content via standardized formats, enabling automated reasoning, data integration, and intelligent agents across decentralized sources.

RDF (Resource Description Framework) models data as subject-predicate-object triples (e.g., “Paris” – “capitalOf” – “France”). Turtle offers concise syntax; JSON-LD embeds in web pages; namespaces (e.g., rdf:, owl:) prevent naming conflicts.

RDFS provides basic schema: subClassOf hierarchies, domain/range constraints for properties. OWL extends with advanced logic—classes (intersection/union), properties (transitive/functional), restrictions (minCardinality, hasValue) for complex knowledge representation.

SPARQL language mimics SQL: SELECT retrieves variables; ASK tests existence; CONSTRUCT generates RDF. Patterns use ?variables, OPTIONAL, FILTER (regex, > operators); federated SERVICE queries span endpoints like DBpedia/Wikidata.

Forward chaining applies rules iteratively to facts, deriving new triples until saturation (bottom-up, efficient for large datasets). Backward chaining starts from queries, working backward through rules to find supporting facts (top-down, goal-driven for sparse results). OWL inference rules (e.g., rdfs:subClassOf transitivity, owl:equivalentClass) automatically deduce class memberships, property chains, and disjointness. Consistency checks detect contradictions like unsatisfiable classes or property violations using DL reasoners (HermiT, Pellet).

URI dereferencing uses HTTP GET on identifiers to retrieve RDF descriptions (303 redirects for non-info resources, content negotiation for formats). Vocabularies: Dublin Core (dc:title, dc:creator for metadata), schema.org (structured data for SEO/search engines), FOAF (foaf:name, foaf:knows for social/person data)—standard terms for interoperability.

Protégé is a free ontology editor with OWL visualization, DL reasoning plugins, and SPARQL queries—ideal for design/debugging. Apache Jena provides Java frameworks for RDF manipulation, OWL reasoning, SPARQL endpoints, and Fuseki server. GraphDB is Ontotext’s RDF triple store with Workbench UI, plugin support (full-text search, geospatial), and cluster scalability.

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  • Helpful course with structured content. Good foundation before diving into more advanced semantic technologies.
    Anna Kowalska
    Designer
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