Lab: OWL 1
Lab 7: RDFS Plus / Basic OWL
Basic OWL ontology programming with RDFlib and owlrl.
- 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.
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".)
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))
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.