Cookbook: Ontology Mapping and Alignment

Goal. You import data from an external dataset (Wikidata, schema.org, an industry vocabulary) and want it to align with your internal ontology — same concepts, different IRIs. Manual mapping is tedious; KGE-driven candidate generation plus SHACL gates plus owl:sameAs canonicalization makes the job tractable.

Why pg_ripple. Composes the same building blocks as record linkage, but applied to classes and properties instead of individuals.

Time to first result. ~25 minutes.

The challenge

Your internal ontology calls a person intkb:Employee. Wikidata calls them wd:Q5 (human). Schema.org calls them schema:Person. None of these is wrong — they describe overlapping but not identical concepts. You want SPARQL queries to transparently see them as the same class for retrieval, but you also want to retain the source-specific axioms.

Step 1 — Load all three vocabularies

-- Internal ontology.
SELECT pg_ripple.load_turtle_into_graph('https://example.org/ontologies/intkb', $TTL$
@prefix intkb: <https://intkb.example/> .
@prefix rdfs:  <http://www.w3.org/2000/01/rdf-schema#> .

intkb:Employee  rdfs:label "Employee" ;
                rdfs:comment "An employee of the organisation" .
intkb:Department rdfs:label "Department" .
intkb:reports   rdfs:label "reports to" .
$TTL$);

-- Schema.org subset.
SELECT pg_ripple.load_turtle_file_into_graph(
    'https://example.org/ontologies/schemaorg', '/data/schemaorg-people.ttl');

-- Wikidata subset.
SELECT pg_ripple.load_turtle_file_into_graph(
    'https://example.org/ontologies/wikidata',  '/data/wikidata-people.ttl');

Step 2 — Train KGE across all three

SET pg_ripple.kge_enabled = on;
SELECT pg_ripple.kge_train(model := 'RotatE', dimensions := 200, epochs := 200);

RotatE is preferred for ontology alignment because it captures inverse and symmetric patterns common in owl:inverseOf axioms.

Step 3 — Generate candidate alignments

SELECT * FROM pg_ripple.find_alignments(
    source_graph := 'https://example.org/ontologies/intkb',
    target_graph := 'https://example.org/ontologies/schemaorg',
    threshold    := 0.85
)
ORDER BY similarity DESC;

-- Same against Wikidata.
SELECT * FROM pg_ripple.find_alignments(
    source_graph := 'https://example.org/ontologies/intkb',
    target_graph := 'https://example.org/ontologies/wikidata',
    threshold    := 0.85
)
ORDER BY similarity DESC;

Step 4 — Gate with structural SHACL

You only want to align classes with classes and properties with properties. A SHACL shape blocks cross-kind alignment errors:

SELECT pg_ripple.load_shacl($TTL$
@prefix sh:  <http://www.w3.org/ns/shacl#> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .

# An owl:equivalentClass link must point Class → Class.
[] a sh:NodeShape ;
   sh:targetSubjectsOf owl:equivalentClass ;
   sh:property [ sh:path rdf:type ; sh:hasValue owl:Class ] .

# An owl:equivalentProperty link must point Property → Property.
[] a sh:NodeShape ;
   sh:targetSubjectsOf owl:equivalentProperty ;
   sh:property [ sh:path rdf:type ;
                 sh:in ( owl:ObjectProperty owl:DatatypeProperty owl:AnnotationProperty ) ] .
$TTL$);

ALTER SYSTEM SET pg_ripple.shacl_mode = 'sync';
SELECT pg_reload_conf();

Step 5 — Apply alignments

For exact equivalences, use owl:equivalentClass / owl:equivalentProperty. For broader relationships, use rdfs:subClassOf / rdfs:subPropertyOf. The OWL 2 RL rule set will then propagate inferences in both directions.

-- High-confidence exact equivalences.
SELECT pg_ripple.insert_triple(
    s1, '<http://www.w3.org/2002/07/owl#equivalentClass>', s2
)
FROM pg_ripple.find_alignments(
    'https://example.org/ontologies/intkb',
    'https://example.org/ontologies/schemaorg', 0.95);

-- Mid-confidence: subclass instead of equivalent.
SELECT pg_ripple.insert_triple(
    s1, '<http://www.w3.org/2000/01/rdf-schema#subClassOf>', s2
)
FROM pg_ripple.find_alignments(
    'https://example.org/ontologies/intkb',
    'https://example.org/ontologies/schemaorg', 0.85)
WHERE similarity < 0.95;

Step 6 — Materialise the inference

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

After this, a SPARQL query for ?p a schema:Person returns every entity that is intkb:Employee (and vice versa for the equivalent classes) with no application-level glue.

Choosing equivalence vs subclass

A bidirectional owl:equivalentClass is a strong claim — both classes have exactly the same instances. If the source vocabularies disagree on edge cases (e.g. minor employee vs human), the weaker rdfs:subClassOf is safer.

See also

• Knowledge-Graph Embeddings
• OWL 2 Profiles
• Record Linkage — the individual counterpart to ontology alignment.