Lab: Getting started with VSCode, Python and RDFlib

Topics

1. Prepare for programming knowledge graphs with rdflib in Python.
2. Get started with basic RDF programming.

Useful materials

VSCode:

• Getting Started with Python in VS Code
• Using Python environments in VS Code (pip is easiest for a start)

RDFlib:

• Intro to RDFlib
• Intro to creating triples
• RDF Terms
• Namespaces and Bindings

RDFlib classes/interfaces and methods:

• Graph (add and perhaps remove methods)
• URIRef
• Literal
• Namespace
• perhaps RDF (the RDF.type field)
• perhaps BNode
• perhaps Collection

Documentation for all the RDFlib modules can be found here. Browser search (often Ctrl-F) can be useful to find the module that you want.

Tasks

Install Python, Pip, VSCode, and RDFlib

You need to have Python version >= 3.7 on your computer. Use the command python --version in a command/terminal window to check. You can download Python and Pip here. To ensure you have the most recent Pip, you can do

python -m pip install --upgrade pip

You need to have an Integrated Development Environment (IDE) that supports Python. If you are unsure, you can download the free and open source Visual Studio Code (VSCode)here.

Create a folder for your INFO216 exercises. Start VSCode and create a new project (from the File menu) by opening your exercise folder folder. Create a new file with .py extension.

Go to your INFO216 exercise folder. You can do this in a command/terminal window outside VScode or use the Terminal menu to create a terminal inside VScode. In the terminal, create and activate a virtual environment. It is easiest to use pip, but it is ok to use pipenv or conda of you prefer.

python -m venv venv

You can create the folder and file in the terminal or in windows explorer. Install Microsoft's Python extension in the VScode extension manager. When the Python extension is installed you can use the 'select interpreter' field on the bottom left to use the virtual environment you made, or make sure you are using a supported version of Python.

In the terminal, and inside your Python environment, install RDFlib:

pip install rdflib

You can now import rdflib into your .py file, or import specific classes/inferfaces such as from rdflib import Namespace, Graph.

Programming tasks

Task: Write a program that creates an RDF graph containing the triples from the sentences below:

• The Mueller Investigation was lead by Robert Mueller.
  • It involved Paul Manafort, Rick Gates, George Papadopoulos, Michael Flynn, Michael Cohen, and Roger Stone.
• Paul Manafort was business partner of Rick Gates.
  • He was campaign chairman for Donald Trump
  • He was charged with money laundering, tax evasion, and foreign lobbying.
  • He was convicted for bank and tax fraud.
  • He pleaded guilty to conspiracy.
  • He was sentenced to prison.
  • He negotiated a plea agreement.
• Rick Gates was charged with money laundering, tax evasion and foreign lobbying.
  • He pleaded guilty to conspiracy and lying to FBI.

Tips: Note that some sentences can result in several triples. For the URIs, you can just use an example path like http://example.org/. So if you want to represent Donald Trump, the URI could be http://example.org/Donald_Trump. Remember that Namespaces can be used so that you don't have to write the full URI every time.

Task: Use the serialize method to write out the model in different formats (on screen or to file):

• Turtle (format='ttl')
• N-Triple (format='nt')
• JSON-LD (format='json-ld')
• RDF-XML (format='xml')

Which one is easiest to read? What are the pros and cons of the different formats? We will look more at some of them later in the course!

Task; Use the online RDF grapher to visualise your model.

Task: Loop through the triples in the model to print out all triples that have pleading guilty as predicate. If you have been inconsistent about some predicate or other term, you can first write loops that correct wrong terms everywhere in the model. (Tip: to correct a term in a model, you typically have to first remove the old triple and then add a new one.)

If you have more time...

Task: If you have more time you can continue extending 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 modify your knowledge graph to account for this?

Task: Write a method (function) that submits your model to https://www.ldf.fi/service/rdf-grapher for rendering and saves the returned image to file.