Lab: OWL 1

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Lab 7: RDFS Plus


RDF Plus sketching. WebVOWL visualisation. Basic OWL ontology programming in Jena.


Tutorial for the Jena-programming part:

Classes/interfaces (some in package org.apache.jena.ontology)

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

Note that the OntModel interface extends InfModel and Model.


In earlier labs you modelled and programmed the following situations:

In RDF: "Cade Tracy lives in 1516 Henry Street, Berkeley, California 94709, USA. He has a B.Sc. in biology from the University of California, Berkeley from 2011. His interests include birds, ecology, the environment, photography and travelling. He has visited Canada and France. Emma Dominguez lives in Carrer de la Guardia Civil 20, 46020 Valencia, Spain. She has a M.Sc. in chemistry from the University of Valencia from 2015. Her areas of expertise include waste management, toxic waste, air pollution. Her interests include bike riding, music and travelling. She has visited Portugal, Italy, France, Germany, Denmark and Sweden. Cade knows Ines. They met in Paris in August 2014."

In RDFS: "University of California, Berkeley and University of Valencia are both Universities. All universities are higher education instituttions (HEIs). Having a B.Sc. from a HEI and having a M.Sc. from a HEI are special cases of gradutating from that HEI. That a person has a degree in a subject means that the person has expertise in that subject. Only persons can have expertise, and what they have expertise about is always a subject."

Extend the RDF and RDFS graphs from earlier to account for the following situation: 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 (dbr:United_States) and the GeoNames resource (gn:6252001). The person class (the RDF type the Cade and Emma resources) in your graph is the same as FOAF's,'s and AKT's person classes (they are,, and, 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 ( No two US cities can have the same postal code. The property you have used for Ines living in Valencia is the same property as FOAF's based near-property (, and it is the inverse of DBpedia's hometown property (, dbo:hometown). (This is not completely precise: but "hometown" is perhaps the inverse of a subproperty of "based near".)

Look through your graph and try to identify at least one of each: a reflexive and an irreflexive, a symmetric and an asymmetric, as well as a transitive property.

Create and output the OWL ontology in RDFlib. If you can, try to build on your example from labs 2 and 3!

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

Use OntModel.writeAll() 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?