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.)