Lab: RDF programming with RDFlib

Topics

RDF graph programming with RDFlib

Useful materials

RDFLib:

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.)