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Lab 5: RDFS Programming with rdflib and owlrl


Basic RDFS graph programming in RDFlib. Entailments and axioms with owlrl.


owlrl.RDFSClosure (RDFS_Semantics, closure, flush_stored_triples)



RDFS.subClassOf, RDFS.subPropertyOf, RDFS.domain, RDFS.range, RDFS.label, RDFS.comment,


First, pip install owlrl. The RDFS Vocabulary can be imported from rdflib.namespace, just like FOAF or RDF.

Consider the following Scenario: "University of California and University of Valencia are both Universities. All universities are higher education institutions (HEIs). Only persons can have an expertise, and what they have expertise in is always a subject. Only persons can graduate from a HEI. If you are a student, you are in fact a person as well. That a person is married to someone, means that they know them. Finally, if a person has a name, that name is also the label of that entity."

Create RDFS triples corresponding to the text above with RDFlib - if you can, try to build on your example from lab 2!

To create the graph in python, you can just use the g.add syntax as we have done previously, or you can use the following code sample to parse a file into a graph:

from rdflib import Graph, Namespace
import owlrl

# Create the graph
g = Graph()

# Parse input data into the graph, format is dependent on the file format. Here turtle (ttl). And location is the path to the local file
g.parse(location="input.ttl", format="turtle")

Using these three lines we can add automatically the inferred triples (like ex:University rdf:type ex:Higher_Education_Institute) :

rdfs = owlrl.RDFSClosure.RDFS_Semantics(g, False, False, False)

After you have done this, try to add the following scenario to you graph as well: "Having a degree from a HEI means that you have also graduated from that HEI. That a city is a capital of a country means that this city is located in that country. That someone was involved in a meeting, means that they have met the other participants. If someone partook in a meeting somewhere, means that they have visited that place" To do this, you will have to swap out the line

rdfs = owlrl.RDFSClosure.RDFS_Semantics(g, False, False, False)


rdfs = owlrl.OWLRL.OWLRL_Semantics(g, False, False, False)

As some of these triples require more advanced reasoning.

Check that simple inference works - make sure that your graph contains triples like these, even if you have not asserted them explicitly:

  • that University of California and Valencia are HEIs
  • that Cade, Emma, and Mary are all persons
  • that Cade and Emma have both graduated from some HEI
  • that Cade knows Mary

One way to check if the triples are there:

universities = g.query("""
PREFIX ex: <>
    ex:University_of_California rdf:type ex:Higher_Education_Institution.

Rewrite some of your existing code to use rdfs:label in a triple and add an rdfs:comment to the same resource.

If you have more time...

Create a new RDFS graph that wraps an empty graph. This graph contains only RDFS axioms. Write it out in Turtle and check that you understand the meaning and purpose of each axiom.

Create an RDF (not RDFS) graph that contains all the triples in your first graph (the one with all the people and universities). Subtract all the triples in the axiom graph from the people/university graph. Write it out to see that you are left with only the asserted and entailed triples and that none of the axioms remain.

Useful Readings