Lab: OWL 1

Lab 7: RDFS Plus / Basic OWL

Topics

Basic OWL ontology programming with RDFlib and owlrl.

WebVOWL visualisation.

Classes/Vocabularies

• OWL (sameAs, equivalentClass, equivalentProperty, differentFrom, disjointWith, inverseOf)
• OntModel (createClass, createIndividual, createObjectProperty, CreateDatatypeProperty, createAllDifferent, createSymmetricProperty, createTransitiveProperty, createInverseFunctionalProperty)
• OntClass, Individual, DatatypeProperty, ObjectProperty

Note that the OntModel interface extends InfModel and Model.

Tasks

Task 1

Write OWL triples that corresponds to the following text. .If you can, try to build on your example from labs 2 and 3!

Cade and Emma are two different persons. All the countries mentioned above are different. The country USA above is the same as the DBpedia resource http://dbpedia.org/resource/United_States (dbr:United_States) and the GeoNames resource http://sws.geonames.org/6252001/ (gn:6252001). The person class (the RDF type the Cade and Emma resources) in your graph is the same as FOAF's, schema.org's and AKT's person classes (they are http://xmlns.com/foaf/0.1/Person, http://schema.org/Person, and http://www.aktors.org/ontology/portal#Person, respectively. Nothing can be any two of a person, a university, or a city at the same time. The property you have used in your RDF/RDFS graph to represent that 94709 is the US zip code of Berkeley, California in US is a subproperty of VCard's postal code-property (http://www.w3.org/2006/vcard/ns#postal-code). No two US cities can have the same postal code. The property you have used for Emma living in Valencia is the same property as FOAF's based near-property (http://xmlns.com/foaf/0.1/based_near), and it is the inverse of DBpedia's hometown property (http://dbpedia.org/ontology/hometown, dbo:hometown). (This is not completely precise: but "hometown" is perhaps the inverse of a subproperty of "based near".)

Task 2

g.add((ex.Cade, ex.married, ex.Mary))
g.add((ex.Cade, ex.livesWith, ex.Mary))
g.add((ex.Cade, ex.sibling, ex.Andrew))
g.add((ex.Cade, ex.sibling, ex.Anna))
g.add((ex.Cade, ex.hasFather, ex.Bob))
g.add((ex.Bob, ex.fatherOf, ex.Cade))

Look through the predicates(properties) above and add new triples for each one that describes them as any of the following: a reflexive , irreflexive, symmetric, asymmetric, transitive, or a functional property. e.g

g.add((ex.married, RDF.type, OWL.SymmetricProperty))

Task 3

Use owlrl to write out the whole ontology, including OWL's built-in axioms (note that sending it to WebVOWL may not work.) Add a reasoner to your OntModel, for example ModelFactory.createOntology(OntModelSpec.OWL_MEM_RULE_INF), and writeAll() again. Can you spot any inferences?

# These three lines add inferred triples to the graph.
owl = owlrl.CombinedClosure.RDFS_OWLRL_Semantics(g, False, False, False)
owl.closure()
owl.flush_stored_triples()

If you have more time...

Write the ontology to a TURTLE file, and try to visualise it using http://visualdataweb.de/webvowl/ . WebVOWL is oriented towards visualising classes and their properties, so the individuals may not show.