OWL 2 Profiles — RL, EL, QL

OWL 2 is too expressive to evaluate in full inside a database — full OWL is undecidable for some queries. The W3C therefore standardised three OWL 2 profiles that trade expressiveness for tractability. pg_ripple ships built-in rule sets and query rewriters for all three.

A single GUC selects the active profile for new connections:

ALTER SYSTEM SET pg_ripple.owl_profile = 'rl';   -- 'rl' | 'el' | 'ql' | 'off'
SELECT pg_reload_conf();

Setting owl_profile = 'off' disables ontology rewriting — useful when you want to inspect raw triples without inference smoothing.

OWL 2 RL — the workhorse

OWL 2 RL is the profile most users want. It covers:

• Class hierarchy: rdfs:subClassOf, owl:equivalentClass, owl:disjointWith
• Property hierarchy: rdfs:subPropertyOf, owl:equivalentProperty, owl:propertyChainAxiom
• Property characteristics: owl:TransitiveProperty, owl:SymmetricProperty, owl:InverseOf, owl:FunctionalProperty, owl:InverseFunctionalProperty
• Class constructors: owl:unionOf, owl:intersectionOf, owl:hasValue, owl:someValuesFrom (in restricted positions)
• owl:sameAs and owl:differentFrom

Run it with:

SELECT pg_ripple.load_rules_builtin('owl-rl');
SELECT pg_ripple.infer('owl-rl');

Performance: the OWL 2 RL rule set has ~80 rules. On a 10 M-triple graph with a typical 1:1 T-Box / A-Box ratio, a full materialisation takes seconds with parallel stratum evaluation enabled.

pg_ripple is 100 % conformant with the W3C OWL 2 RL test suite — see OWL 2 RL Conformance Results.

OWL 2 EL — for large terminologies

EL was designed for ontologies with very large class hierarchies and few individuals — medical terminologies (SNOMED, NCIt), gene ontologies, product taxonomies. It supports:

• Class subsumption with existential restrictions: owl:someValuesFrom
• Property chains: e.g. partOf ∘ partOf ⊑ partOf
• owl:hasSelf
• Reflexive properties

EL classification (computing the full subclass hierarchy) is polynomial in the size of the ontology. pg_ripple's EL implementation uses a saturation-based algorithm and stores the closure in _pg_ripple.el_classified.

SELECT pg_ripple.load_rules_builtin('owl-el');
SELECT pg_ripple.infer('owl-el');

-- All classes that subsume :BacterialPneumonia.
SELECT * FROM pg_ripple.sparql('
    SELECT ?super WHERE { <https://example.org/BacterialPneumonia>
                          <http://www.w3.org/2000/01/rdf-schema#subClassOf>+ ?super }
');

OWL 2 QL — for query rewriting

QL is the profile of choice when you want to answer SPARQL queries over an ontology without materialising inferences first. Instead of expanding the data, pg_ripple rewrites the query at translation time using the ontology axioms. This keeps the data store small and lets you change the ontology without re-materialising.

QL supports a deliberately small set of constructs — rdfs:subClassOf, rdfs:subPropertyOf, owl:inverseOf, owl:someValuesFrom (in object position only), owl:disjointWith. The trade-off is that everything is fast.

SELECT pg_ripple.load_rules_builtin('owl-ql');
SET pg_ripple.owl_profile = 'ql';

-- This SELECT is rewritten using subClassOf axioms before execution.
SELECT * FROM pg_ripple.sparql('
    SELECT ?animal WHERE { ?animal a <https://example.org/Mammal> }
');

If Dog rdfs:subClassOf Mammal, the rewriter expands Mammal into (Mammal | Dog | Cat | …) automatically — without inserting any new triples.

SPARQL-DL — direct OWL axiom queries

When you want to query the T-Box itself (the ontology, not the data), pg_ripple exposes two SPARQL-DL helpers:

-- Direct subclasses of :Mammal.
SELECT * FROM pg_ripple.sparql_dl_subclasses('<https://example.org/Mammal>');

-- All superclasses of :Dog.
SELECT * FROM pg_ripple.sparql_dl_superclasses('<https://example.org/Dog>');

These route OWL vocabulary BGPs (owl:subClassOf, owl:equivalentClass, owl:disjointWith) directly to T-Box VP tables — no separate index required.

Choosing a profile

You can also load multiple profiles' rule sets at once and run them under different rule-set names — they are independent.

See also

• Reasoning & Inference — the Datalog engine that powers all three profiles.
• OWL 2 RL Conformance Results
• SPARQL Compliance Matrix