The Semantic Web And Ontologies#

This chapter introduces RDF, JSON-LD, RDFS, SKOS and OWL from one small example. These technologies overlap, but they do different jobs.

The Goal#

Ordinary JSON can carry a field named publisher:

{
  "title": "Monthly House Prices",
  "publisher": "Example Statistics Office"
}

Another system might use owner, publishingBody or organisation. A person can infer that the fields may be related, but software needs shared identifiers and declared mappings.

The semantic web approach gives things and properties global identifiers, then represents statements as a graph.

RDF: The Graph Model#

Resource Description Framework, or RDF, represents a statement as a triple:

subject          predicate       object
dataset/house    dcterms:title   "Monthly House Prices"

The three positions mean:

• subject — the thing being described;
• predicate — the property or relationship;
• object — another identified thing or a literal value.

An object can be:

• an IRI identifying another resource;
• a string, number, date or other typed literal;
• a language-tagged string.

Several triples sharing subjects and objects form a graph.

Literals And Resources#

These statements are different:

dataset/house --publisher name--> "Example Statistics Office"
dataset/house --publisher-------> publisher/example-office

The first ends in text. The second ends in a resource that can have its own identifier, names, homepage and relationships.

Use a literal for a value. Use an identified resource when identity and further description matter.

Serialisation#

RDF is an abstract graph model, not one file syntax. It can be written as Turtle, JSON-LD and other serialisations.

Turtle#

Turtle is compact and readable for semantic definitions:

@prefix dcterms: <http://purl.org/dc/terms/> .

<https://example.org/dataset/house>
  dcterms:title "Monthly House Prices" ;
  dcterms:publisher <https://example.org/publisher/example-office> .

JSON-LD#

JSON-LD adds linked-data meaning to JSON:

{
  "@context": {
    "title": "http://purl.org/dc/terms/title",
    "publisher": {
      "@id": "http://purl.org/dc/terms/publisher",
      "@type": "@id"
    }
  },
  "@id": "https://example.org/dataset/house",
  "title": "Monthly House Prices",
  "publisher": "https://example.org/publisher/example-office"
}

The @context maps short JSON keys to IRIs and states that publisher points to another identifier.

JSON-LD can be:

• expanded, using full semantic identifiers;
• compacted, using context terms;
• flattened or framed for particular JSON arrangements.

These can describe the same RDF graph. The visible JSON shape alone is not the complete semantics.

Pinned Contexts#

A remote JSON-LD context can change or become unavailable. It can also cause a parser to make unexpected network requests.

The semantic builder uses pinned, reviewed contexts for deterministic and safe processing. A published context version is part of the data contract.

RDFS: Basic Vocabulary Structure#

RDF Schema, or RDFS, supplies terms for describing classes and properties. Important terms include:

• rdfs:Class;
• rdf:Property;
• rdfs:subClassOf;
• rdfs:subPropertyOf;
• rdfs:domain;
• rdfs:range;
• rdfs:label;
• rdfs:comment.

Suppose:

PublicDataset subClassOf Dataset
publishedBy subPropertyOf contributor

If an item is a PublicDataset, RDFS reasoning can also treat it as a Dataset. If an edge uses publishedBy, it can also satisfy a query for contributor.

Domain And Range Are Not Form Validation#

If publishedBy has domain Dataset and range Agent, using that property can entail types for its subject and object.

It does not mean “reject any input whose type field is missing.” Validation is a different layer.

SKOS: Concept Schemes#

Simple Knowledge Organization System, or SKOS, is intended for thesauri, taxonomies and controlled concept schemes.

Useful terms include:

• preferred, alternative and hidden labels;
• broader and narrower concepts;
• related concepts;
• membership in a concept scheme;
• exact, close, broad and narrow mappings between schemes.

SKOS is a good fit for:

• topics;
• licence families;
• format families;
• geography classifications;
• organisational classifications.

Example:

concept/london
  preferred label "London"
  broader concept concept/england
  in scheme geography/uk

A source tag remains a keyword until it is mapped to a governed concept. Similar spelling does not automatically create skos:exactMatch.

OWL: More Expressive Semantics#

Web Ontology Language, or OWL, can express richer class and property semantics.

Examples include:

• equivalent classes or properties;
• inverse properties;
• transitive or symmetric properties;
• restrictions on class membership;
• property chains;
• identity with owl:sameAs.

These declarations support inference: deriving statements that were not written explicitly but follow from the rules.

Example Inference#

Suppose:

publishedBy inverseOf publishes
houseDataset publishedBy exampleOffice

A reasoner can entail:

exampleOffice publishes houseDataset

The entailed statement should carry its derivation rule and source assertions if it is materialised into an OKF publication.

OWL Profiles And Bounded Reasoning#

Full ontology reasoning can be expensive and difficult to explain. OWL defines profiles suited to different needs. The architecture proposes a deliberately bounded, OWL 2 RL-style rule set.

The builder can materialise selected entailments: calculate them once, publish them as derived edges and record how they were produced. The browser then filters and displays those edges without running an unrestricted description-logic reasoner.

This supports static reliability and auditability.

Open-World Assumption#

RDF, RDFS and OWL usually use the open-world assumption:

Not knowing a statement does not make the statement false.

If a dataset has no published licence triple, the graph says the licence is unknown in this graph. It does not prove there is no licence.

This differs from many forms and database applications, where a missing required field is an error under a closed publication contract.

Both views are useful:

• semantic reasoning remains open to additional facts;
• validation checks whether this particular publication supplied required information.

No Unique-Name Assumption#

Two different IRIs are not automatically known to describe different things. Conversely, they are not automatically the same.

Identity needs an explicit, justified statement. owl:sameAs is powerful because every fact about one identity can apply to the other. It must not be inferred from a matching label, shared URL string or model confidence.

Inconsistency Versus Validation Failure#

An ontology can contain logical statements that cannot all be true together. That is an ontology inconsistency.

A record can fail a rule such as “exactly one title must be published.” That is a validation failure.

The two are not interchangeable. A SHACL failure does not automatically prove the OWL ontology inconsistent.

The Five Layers Revisited#

PROV describes how statements and artefacts were produced. DCAT supplies domain terms for data catalogues. They are introduced in the next chapter.

What The Explorer Implements Now#

The current Svelte Explorer implements predicate-aware focus graph presentation and compatibility with label-only relationships. It does not claim that every loaded bundle is an ontology, and it does not perform unbounded OWL inference in the browser.

The advanced semantic extension, vocabulary registry, validation views and materialised inference are a delivery roadmap. Beginner documentation must not turn proposals into current behaviour.

Next#

Validation, provenance and catalogue standards explains how a publication is checked and how evidence survives transformation.