Lab: RDF programming with RDFlib: Difference between revisions

Latest revision as of 13:20, 28 January 2024

Topics

RDF graph programming with RDFlib

Useful materials

RDFLib:

Lab Presentations:

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.

If you want, you can try to use properties and types from standard vocabularies like FOAF (friend-of-a-friend) and DC (Dublin Core), but this is something we will look at in later exercises.

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: It is possible to solve the task above without blank (or anonymous nodes). But to do so, you need to create a URI for each "instance of lying". This is a situation where blank nodes may be more suitable. Change your graph so it represents instances of lying as blank nodes.

Task: Save (serialize) your graph to a Turtle file. Add a few triples to the Turtle file with more information about Donald Trump. For example, you can add that Donald Trump is married to Melania and has several children. You can also use blank nodes to represent two of Trump's addresses when he was president:

The White House, 1600 Pennsylvania Ave., NW Washington, DC 20500, United States, phone: 1-202-456-1414
Mar-a-Lago Club, 1100 S Ocean Blvd, Palm Beach, FL 33480, United States

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. An excerpt of 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 <== and ==> arrows are printed to indicate the reverse of a property. We do that with a print() statement in Python, not from inside rdflib.

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.)

@@ Line 1: / Line 1: @@
+==Topics==
+* RDF graph programming with RDFlib
-=Lab 2: RDF programming with RDFlib=
+==Useful materials==
+RDFLib:
+* [https://rdflib.readthedocs.io/en/stable/intro_to_creating_rdf.html Creating Triples]
+* [https://rdflib.readthedocs.io/en/stable/rdf_terms.html RDF Terms]
+* [https://rdflib.readthedocs.io/en/stable/namespaces_and_bindings.html Namespaces and Bindings]
+* [https://rdflib.readthedocs.io/en/stable/intro_to_graphs.html Navigating Graphs]
+* [https://rdflib.readthedocs.io/en/stable/intro_to_parsing.html Serialising and parsing]
-==Topics==
+Lab Presentations:
-* Basic RDF graph programming with RDFlib.
+* [https://docs.google.com/presentation/d/1blXlTTTsL8jqeV5sRLhQuZ-nssNZqH_bjSgjj-kougE/edit?usp=sharing Lab 1 - RDF Presentation]
-* Simple reading/writing from/to file.
+* [https://docs.google.com/presentation/d/17yuNqn66fhEIHPE65PyFC0H359q1k6CquE6ojDV0NB4/edit?usp=sharing Lab 2 - More RDF Presentation]
-* Simple looping trough graph
-* Setting up groups for the group project
+RDFlib classes/interfaces:
+* from rdflib import Graph, Namespace, URIRef, BNode, Literal
+* from rdflib.namespace import RDF, FOAF, XSD
+* from rdflib.collection import Collection
-==Classes/interfaces==
+RDFlib methods:
-import [https://rdflib.readthedocs.io/en/stable/py-modindex.html rdflib]:
+* Graph: add(), remove(), triples(), serialize(), parse(), bind()
-*Graph (add, remove, serialize, parse)
-*Namespace
-*URIRef
-*Literal
-*Bnode
-*RDF, FOAF, XSD
 ==Tasks==
-Consider the following situation:
+Continue with the graph you created in [[Lab: Getting started with VSCode, Python and RDFlib | Exercise 1]].
-"Cade lives in 1516 Henry Street, Berkeley, California 94709,
-USA. He has a B.Sc. in biology from the University of California,
+'''Task:''' Continue to extend your graph:
-Berkeley from 2011. His interests include birds, ecology, the
+* Michael Cohen was Donald Trump's attorney.
-environment, photography and travelling. He has visited Canada and
+** He pleaded guilty for lying to Congress.
-France. Emma Dominguez lives in Carrer de la Guardia Civil 20, 46020
+* Michael Flynn was adviser to Donald Trump.
-Valencia, Spain. She has a M.Sc. in chemistry from the University of
+** He pleaded guilty for lying to the FBI.
-Valencia from 2015. Her areas of expertise include waste management,
+** He negotiated a plea agreement.
-toxic waste, air pollution. Her interests include bike riding, music
+If you want, you can try to use properties and types from standard vocabularies like FOAF (friend-of-a-friend) and DC (Dublin Core), but this is something we will look at in later exercises.
-and travelling. She has visited Portugal, Italy, France, Germany,
-Denmark and Sweden. Cade knows Emma. They met in Paris in August
+'''Task:''' According to [https://www.pbs.org/wgbh/frontline/article/the-mueller-investigation-explained-2/ 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?
-(Make up your own URIs when you need to (like "http://example.org/"), or even better: use terms
+* How can you modify your knowledge graph to account for this?
-you know from vocabularies such as FOAF and RDF.
-Create a graph in RDFlib with triples corresponding to the text above.
+'''Task:''' It is possible to solve the task above without blank (or anonymous nodes). But to do so, you need to create a URI for each "instance of lying". This is a situation where blank nodes may be more suitable. Change your graph so it represents instances of lying as blank nodes.
-Write out your graph to the console in the following formats: "turtle",
+'''Task:''' Save (''serialize'') your graph to a Turtle file. Add a few triples ''to the Turtle file'' with more information about Donald Trump. For example, you can add that Donald Trump is married to Melania and has several children. You can also use blank nodes to represent two of Trump's addresses when he was president:
-"n3", "nt", "xml". How do they differ? What is the default?
+* The White House, 1600 Pennsylvania Ave., NW Washington, DC 20500, United States, phone: 1-202-456-1414
+* Mar-a-Lago Club, 1100 S Ocean Blvd, Palm Beach, FL 33480, United States
+Visualise the result if you want. Read (''parse'') the Turtle file back into a Python program, and check that the new triples are there.
-Write your graph to a file.
+==If you have more time...==
-Edit the file so that Cade has the middle name Creighton and the middle name of Emma is María.
+'''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. An excerpt of 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
-Create a new program that reads your graph in again from the file and
+Here, the <== and ==> arrows are printed to indicate the reverse of a property. We do that with a ''print()'' statement in Python, not from inside rdflib.
-writes it to the console. Check that the new middle names are there!
-Continuing with either your first or second program, write a loop that
+''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.
-goes through all the triples in the graph and prints them to the
-console.
-Change the loop so that (a) it only loops through triples about
+''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.)
-Emma (b) it only loops through triples involving the names of
-people.
-Remove all addresses from the graph.
+<!--
+==Triples you can extend for the tasks (turtle format)==
+<syntaxhighlight>
+@prefix ex: <http://example.org/> .
-==If you have more time...==
+ex:Mueller_Investigation ex:involved ex:George_Papadopoulos,
+        ex:Michael_Cohen,
+        ex:Michael_Flynn,
+        ex:Paul_Manafort,
+        ex:Rick_Gates,
+        ex:Roger_Stone ;
+    ex:leadBy ex:Robert_Muller .
-Below are four lines of comma-separated values (csv - five lines with
+ex:Paul_Manafort ex:businessManager ex:Rick_Gates ;
-the headers) that could have been saved from a spreadsheet. Copy them
+    ex:campaignChairman ex:Donald_Trump ;
-into a file and write a program with a loop that reads each line from that file (except
+    ex:chargedWith ex:ForeignLobbying,
-the initial header line) and adds it to your graph as triples:
+        ex:MoneyLaundering,
+        ex:TaxEvasion ;
+    ex:convictedFor ex:BankFraud,
+        ex:TaxFraud ;
+    ex:negoiated ex:PleaBargain ;
+    ex:pleadGuiltyTo ex:Conspiracy ;
+    ex:sentencedTo ex:Prison .
-  "Name","Gender","Country","Town","Expertise","Interests"
+ex:Rick_Gates ex:chargedWith ex:ForeignLobbying,
-  "Regina Catherine Hall","F","Great Britain","Manchester","Ecology, zoology","Football, music travelling"
+        ex:MoneyLaundering,
-  "Achille Blaise","M","France","Nancy","","Chess, computer games"
+        ex:TaxEvasion ;
-  "Nyarai Awotwi Ihejirika","F","Kenya","Nairobi","Computers, semantic networks","Hiking, botany"
+    ex:pleadGuiltyTo ex:Conspiracy,
-  "Xun He Zhang","M","China","Chengdu","Internet, mathematics, logistics","Dancing, music, trombone"
+        ex:LyingToFBI .
-In the resulting graph, delete all information about Achille.
+</syntaxhighlight>
+-->