Lab: RDF programming with RDFlib

Topics

• RDF graph programming with RDFlib

Useful materials

RDFLib:

• Creating Triples
• Navigating Graphs
• Parsing

RDFlib classes/interfaces:

• from rdflib import Graph, Namespace, URIRef, BNode, Literal
• from rdflib.namespace import RDF, FOAF, XSD
• from rdflib.collection import Collection

RDFlib methods:

• Graph: add(), remove(), triples(), serialize(), parse(), bind()

Tasks

Continue with the graph you created in Exercise 1.

Task: Continue to extend your graph:

• Michael Cohen was Donald Trump's attorney.
  • He pleaded guilty for lying to Congress.
• Michael Flynn was adviser to Donald Trump.
  • He pleaded guilty for lying to the FBI.
  • He negotiated a plea agreement.

Task: According to this FRONTLINE article, Gates', Cohen's and Flynn's lying were different and are described in different detail.

• How can you represent "different instances of lying" as triples?
• How can you modify your knowledge graph to account for this?

Task: Save (serialize) your graph to a Turtle file. Add a few triples with more information about Donald Trump. Visualise the result if you want. Read (parse) the Turtle file back into a Python program, and check that the new triples are there.

If you have more time...

Task: Write a method (function) that starts with Donald Trump prints out a graph depth-first to show how the other graph nodes are connected to him. For example, the output could be:

ex:Donald_Trump
    ^ex:campaignManager ex:Paul_Manafort
        ex:convictedFor ex:BankAndTaxFraud
        ...
    ^ex:attorneyFor ex:Michael_Cohen
        ex:pleadedGuilty ex:LyingToCongress

Here, the ^-sign is used to indicate the reverse of a property.

Note: Because you must follow triples in both subject-to-predicate and predicate-to-subject direction, you must keep a list of already visited nodes, and never return to a previously visited one.

Note: If you want a neat solution, it may be best to combine two graph traversals: first traverse the model breadth-first to create a new tree-shaped model, and then traverse the tree-shaped model depth-first to print it out with indentation. (The point of the first breadth-first step is to find the shortest path to each node.)