Lab Solutions
This page will be updated with Python examples related to the labs as the course progresses.
Examples from the lectures
Lecture 1: Introduction to KGs
Turtle example:
@prefix ex: <http://example.org/> .
ex:Roger_Stone
ex:name "Roger Stone" ;
ex:occupation ex:lobbyist ;
ex:significant_person ex:Donald_Trump .
ex:Donald_Trump
ex:name "Donald Trump" .Lecture 2: RDF
Blank nodes for anonymity, or when we have not decided on a URI:
from rdflib import Graph, Namespace, Literal, BNode, RDF, RDFS, DC, FOAF, XSD
EX = Namespace('http://example.org/')
g = Graph()
g.bind('ex', EX) # this is why the line '@prefix ex: <http://example.org/> .'
# and the 'ex.' prefix are used when we print out Turtle later
robertMueller = BNode()
g.add((robertMueller, RDF.type, EX.Human))
g.add((robertMueller, FOAF.name, Literal('Robert Mueller', lang='en')))
g.add((robertMueller, EX.position_held, Literal('Director of the Federal Bureau of Investigation', lang='en')))
print(g.serialize(format='turtle'))
Blank nodes used to group related properties:
from rdflib import Graph, Namespace, Literal, BNode, RDF, RDFS, DC, FOAF, XSD
EX = Namespace('http://example.org/')
g = Graph()
g.bind('ex', EX)
# This is a task in Exercise 2
print(g.serialize(format='turtle'))Literals:
from rdflib import Graph, Namespace, Literal, BNode, RDF, RDFS, DC, FOAF, XSD
EX = Namespace('http://example.org/')
g = Graph()
g.bind('ex', EX)
g.add((EX.Robert_Mueller, RDF.type, EX.Human))
g.add((EX.Robert_Mueller, FOAF.name, Literal('Robert Mueller', lang='en')))
g.add((EX.Robert_Mueller, FOAF.name, Literal('رابرت مولر', lang='fa')))
g.add((EX.Robert_Mueller, DC.description, Literal('sixth director of the FBI', datatype=XSD.string)))
g.add((EX.Robert_Mueller, EX.start_time, Literal(2001, datatype=XSD.integer)))
print(g.serialize(format='turtle'))Alternative container (open):
from rdflib import Graph, Namespace, Literal, BNode, RDF, RDFS, DC, FOAF, XSD
EX = Namespace('http://example.org/')
g = Graph()
g.bind('ex', EX)
muellerReportArchives = BNode()
g.add((muellerReportArchives, RDF.type, RDF.Alt))
archive1 = 'https://archive.org/details/MuellerReportVolume1Searchable/' \
'Mueller%20Report%20Volume%201%20Searchable/'
archive2 = 'https://edition.cnn.com/2019/04/18/politics/full-mueller-report-pdf/index.html'
archive3 = 'https://www.politico.com/story/2019/04/18/mueller-report-pdf-download-text-file-1280891'
g.add((muellerReportArchives, RDFS.member, Literal(archive1, datatype=XSD.anyURI)))
g.add((muellerReportArchives, RDFS.member, Literal(archive2, datatype=XSD.anyURI)))
g.add((muellerReportArchives, RDFS.member, Literal(archive3, datatype=XSD.anyURI)))
g.add((EX.Mueller_Report, RDF.type, FOAF.Document))
g.add((EX.Mueller_Report, DC.contributor, EX.Robert_Mueller))
g.add((EX.Mueller_Report, SCHEMA.archivedAt, muellerReportArchives))
print(g.serialize(format='turtle'))Sequence container (open):
from rdflib import Graph, Namespace, Literal, BNode, RDF, RDFS, DC, FOAF, XSD
EX = Namespace('http://example.org/')
g = Graph()
g.bind('ex', EX)
donaldTrumpSpouses = BNode()
g.add((donaldTrumpSpouses, RDF.type, RDF.Seq))
g.add((donaldTrumpSpouses, RDF._1, EX.IvanaTrump))
g.add((donaldTrumpSpouses, RDF._2, EX.MarlaMaples))
g.add((donaldTrumpSpouses, RDF._3, EX.MelaniaTrump))
g.add((EX.Donald_Trump, SCHEMA.spouse, donaldTrumpSpouses))
print(g.serialize(format='turtle'))Collection (closed list):
from rdflib import Graph, Namespace, Literal, BNode, RDF, RDFS, DC, FOAF, XSD
EX = Namespace('http://example.org/')
g = Graph()
g.bind('ex', EX)
from rdflib.collection import Collection
g = Graph()
g.bind('ex', EX)
donaldTrumpSpouses = BNode()
Collection(g, donaldTrumpSpouses, [
EX.IvanaTrump, EX.MarlaMaples, EX.MelaniaTrump
])
g.add((EX.Donald_Trump, SCHEMA.spouse, donaldTrumpSpouses))
print(g.serialize(format='turtle'))
g.serialize(destination='s02_Donald_Trump_spouses_list.ttl', format='turtle')
print(g.serialize(format='turtle'))Example lab solutions
Getting started (Lab 1)
from rdflib import Graph, Namespace
g = Graph()
ex = Namespace('http://example.org/')
g.bind("ex", ex)
#The Mueller Investigation was lead by Robert Mueller.
g.add((ex.Mueller_Investigation, ex.leadBy, ex.Robert_Muller))
#It involved Paul Manafort, Rick Gates, George Papadopoulos, Michael Flynn, and Roger Stone.
g.add((ex.Mueller_Investigation, ex.involved, ex.Paul_Manafort))
g.add((ex.Mueller_Investigation, ex.involved, ex.Rick_Gates))
g.add((ex.Mueller_Investigation, ex.involved, ex.George_Papadopoulos))
g.add((ex.Mueller_Investigation, ex.involved, ex.Michael_Flynn))
g.add((ex.Mueller_Investigation, ex.involved, ex.Michael_Cohen))
g.add((ex.Mueller_Investigation, ex.involved, ex.Roger_Stone))
# --- Paul Manafort ---
#Paul Manafort was business partner of Rick Gates.
g.add((ex.Paul_Manafort, ex.businessManager, ex.Rick_Gates))
# He was campaign chairman for Trump
g.add((ex.Paul_Manafort, ex.campaignChairman, ex.Donald_Trump))
# He was charged with money laundering, tax evasion, and foreign lobbying.
g.add((ex.Paul_Manafort, ex.chargedWith, ex.MoneyLaundering))
g.add((ex.Paul_Manafort, ex.chargedWith, ex.TaxEvasion))
g.add((ex.Paul_Manafort, ex.chargedWith, ex.ForeignLobbying))
# He was convicted for bank and tax fraud.
g.add((ex.Paul_Manafort, ex.convictedFor, ex.BankFraud))
g.add((ex.Paul_Manafort, ex.convictedFor, ex.TaxFraud))
# He pleaded guilty to conspiracy.
g.add((ex.Paul_Manafort, ex.pleadGuiltyTo, ex.Conspiracy))
# He was sentenced to prison.
g.add((ex.Paul_Manafort, ex.sentencedTo, ex.Prison))
# He negotiated a plea agreement.
g.add((ex.Paul_Manafort, ex.negoiated, ex.PleaBargain))
# --- Rick Gates ---
#Rick Gates was charged with money laundering, tax evasion and foreign lobbying.
g.add((ex.Rick_Gates, ex.chargedWith, ex.MoneyLaundering))
g.add((ex.Rick_Gates, ex.chargedWith, ex.TaxEvasion))
g.add((ex.Rick_Gates, ex.chargedWith, ex.ForeignLobbying))
#He pleaded guilty to conspiracy and lying to FBI.
g.add((ex.Rick_Gates, ex.pleadGuiltyTo, ex.Conspiracy))
g.add((ex.Rick_Gates, ex.pleadGuiltyTo, ex.LyingToFBI))
#Use the serialize method to write out the model in different formats on screen
print(g.serialize(format="ttl"))
# g.serialize("lab1.ttl", format="ttl") #or to file
#Loop through the triples in the model to print out all triples that have pleading guilty as predicate
for subject, object in g[ : ex.pleadGuiltyTo : ]:
print(subject, ex.pleadGuiltyTo, object)
# Michael Cohen, Michael Flynn and the lying is part of lab 2 and therefore the answer is not provided this week
#Write a method (function) that submits your model for rendering and saves the returned image to file.
import requests
import shutil
def graphToImage(graph):
data = {"rdf":graph, "from":"ttl", "to":"png"}
link = "http://www.ldf.fi/service/rdf-grapher"
response = requests.get(link, params = data, stream=True)
# print(response.content)
print(response.raw)
with open("lab1.png", "wb") as fil:
shutil.copyfileobj(response.raw, fil)
graph = g.serialize(format="ttl")
graphToImage(graph)RDF programming with RDFlib (Lab 2)
from rdflib import Graph, URIRef, Namespace, Literal, XSD, BNode
from rdflib.collection import Collection
g = Graph()
g.parse("lab1.ttl", format="ttl") #Retrives the triples from lab 1
ex = Namespace('http://example.org/')
# --- Michael Cohen ---
#Michael Cohen was Donald Trump's attorney.
g.add((ex.Michael_Cohen, ex.attorneyTo, ex.Donald_Trump))
#He pleaded guilty to lying to the FBI.
g.add((ex.Michael_Cohen, ex.pleadGuiltyTo, ex.LyingToCongress))
# --- Michael Flynn ---
#Michael Flynn was adviser to Trump.
g.add((ex.Michael_Flynn, ex.adviserTo, ex.Donald_Trump))
#He pleaded guilty to lying to the FBI.
g.add((ex.Michael_Flynn, ex.pleadGuiltyTo, ex.LyingToFBI))
# He negotiated a plea agreement.
g.add((ex.Michael_Flynn, ex.negoiated, ex.PleaBargain))
#How can you modify your knowledge graph to account for the different lying?
#Remove these to not have duplicates
g.remove((ex.Michael_Flynn, ex.pleadGuiltyTo, ex.LyingToFBI))
g.remove((ex.Michael_Flynn, ex.negoiated, ex.PleaBargain))
g.remove((ex.Rick_Gates, ex.pleadGuiltyTo, ex.LyingToFBI))
g.remove((ex.Rick_Gates, ex.pleadGuiltyTo, ex.Conspiracy))
g.remove((ex.Rick_Gates, ex.chargedWith, ex.ForeignLobbying))
g.remove((ex.Rick_Gates, ex.chargedWith, ex.MoneyLaundering))
g.remove((ex.Rick_Gates, ex.chargedWith, ex.TaxEvasion))
g.remove((ex.Michael_Cohen, ex.pleadGuiltyTo, ex.LyingToCongress))
# --- Michael Flynn ---
FlynnLying = BNode()
g.add((FlynnLying, ex.crime, ex.LyingToFBI))
g.add((FlynnLying, ex.pleadGulityOn, Literal("2017-12-1", datatype=XSD.date)))
g.add((FlynnLying, ex.liedAbout, Literal("His communications with a former Russian ambassador during the presidential transition", datatype=XSD.string)))
g.add((FlynnLying, ex.pleaBargain, Literal("true", datatype=XSD.boolean)))
g.add((ex.Michael_Flynn, ex.pleadGuiltyTo, FlynnLying))
# --- Rick Gates ---
GatesLying = BNode()
Crimes = BNode()
Charged = BNode()
Collection(g, Crimes, [ex.LyingToFBI, ex.Conspiracy])
Collection(g, Charged, [ex.ForeignLobbying, ex.MoneyLaundering, ex.TaxEvasion])
g.add((GatesLying, ex.crime, Crimes))
g.add((GatesLying, ex.chargedWith, Charged))
g.add((GatesLying, ex.pleadGulityOn, Literal("2018-02-23", datatype=XSD.date)))
g.add((GatesLying, ex.pleaBargain, Literal("true", datatype=XSD.boolean)))
g.add((ex.Rick_Gates, ex.pleadGuiltyTo, GatesLying))
# --- Michael Cohen ---
CohenLying = BNode()
g.add((CohenLying, ex.crime, ex.LyingToCongress))
g.add((CohenLying, ex.liedAbout, ex.TrumpRealEstateDeal))
g.add((CohenLying, ex.prosecutorsAlleged, Literal("In an August 2017 letter Cohen sent to congressional committees investigating Russian election interference, he falsely stated that the project ended in January 2016", datatype=XSD.string)))
g.add((CohenLying, ex.mullerInvestigationAlleged, Literal("Cohen falsely stated that he had never agreed to travel to Russia for the real estate deal and that he did not recall any contact with the Russian government about the project", datatype=XSD.string)))
g.add((CohenLying, ex.pleadGulityOn, Literal("2018-11-29", datatype=XSD.date)))
g.add((CohenLying, ex.pleaBargain, Literal("true", datatype=XSD.boolean)))
g.add((ex.Michael_Cohen, ex.pleadGuiltyTo, CohenLying))
print(g.serialize(format="ttl"))
#Save (serialize) your graph to a Turtle file.
# g.serialize("lab2.ttl", format="ttl")
#Add a few triples to the Turtle file with more information about Donald Trump.
'''
ex:Donald_Trump ex:address [ ex:city ex:Palm_Beach ;
ex:country ex:United_States ;
ex:postalCode 33480 ;
ex:residence ex:Mar_a_Lago ;
ex:state ex:Florida ;
ex:streetName "1100 S Ocean Blvd"^^xsd:string ] ;
ex:previousAddress [ ex:city ex:Washington_DC ;
ex:country ex:United_States ;
ex:phoneNumber "1 202 456 1414"^^xsd:integer ;
ex:postalCode "20500"^^xsd:integer ;
ex:residence ex:The_White_House ;
ex:streetName "1600 Pennsylvania Ave."^^xsd:string ];
ex:marriedTo ex:Melania_Trump;
ex:fatherTo (ex:Ivanka_Trump ex:Donald_Trump_Jr ex: ex:Tiffany_Trump ex:Eric_Trump ex:Barron_Trump).
'''
#Read (parse) the Turtle file back into a Python program, and check that the new triples are there
def serialize_Graph():
newGraph = Graph()
newGraph.parse("lab2.ttl")
print(newGraph.serialize())
# serialize_Graph() #Don't need this to run until after adding the triples above to the ttl file
#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
visited_nodes = set()
def create_Tree(model, nodes):
#Traverse the model breadth-first to create the tree.
global visited_nodes
tree = Graph()
children = set()
visited_nodes |= set(nodes)
for s, p, o in model:
if s in nodes and o not in visited_nodes:
tree.add((s, p, o))
visited_nodes.add(o)
children.add(o)
if o in nodes and s not in visited_nodes:
invp = URIRef(f'{p}_inv') #_inv represents inverse of
tree.add((o, invp, s))
visited_nodes.add(s)
children.add(s)
if len(children) > 0:
children_tree = create_Tree(model, children)
for triple in children_tree:
tree.add(triple)
return tree
def print_Tree(tree, root, indent=0):
#Print the tree depth-first.
print(str(root))
for s, p, o in tree:
if s==root:
print(' '*indent + ' ' + str(p), end=' ')
print_Tree(tree, o, indent+1)
tree = create_Tree(g, [ex.Donald_Trump])
print_Tree(tree, ex.Donald_Trump)SPARQL Programming (Lab 4)
NOTE: These tasks were performed on the old dataset, with the new dataset, some of these answers would be different.
from rdflib import Graph, Namespace, RDF, FOAF
from SPARQLWrapper import SPARQLWrapper, JSON, POST, GET, TURTLE
g = Graph()
g.parse("Russia_investigation_kg.ttl")
# ----- RDFLIB -----
ex = Namespace('http://example.org#')
NS = {
'': ex,
'rdf': RDF,
'foaf': FOAF,
}
# Print out a list of all the predicates used in your graph.
task1 = g.query("""
SELECT DISTINCT ?p WHERE{
?s ?p ?o .
}
""", initNs=NS)
print(list(task1))
# Print out a sorted list of all the presidents represented in your graph.
task2 = g.query("""
SELECT DISTINCT ?president WHERE{
?s :president ?president .
}
ORDER BY ?president
""", initNs=NS)
print(list(task2))
# Create dictionary (Python dict) with all the represented presidents as keys. For each key, the value is a list of names of people indicted under that president.
task3_dic = {}
task3 = g.query("""
SELECT ?president ?person WHERE{
?s :president ?president;
:name ?person;
:outcome :indictment.
}
""", initNs=NS)
for president, person in task3:
if president not in task3_dic:
task3_dic[president] = [person]
else:
task3_dic[president].append(person)
print(task3_dic)
# Use an ASK query to investigate whether Donald Trump has pardoned more than 5 people.
# This task is a lot trickier than it needs to be. As far as I'm aware RDFLib has no HAVING support, so a query like this:
task4 = g.query("""
ASK {
SELECT (COUNT(?s) as ?count) WHERE{
?s :pardoned :true;
:president :Bill_Clinton .
}
HAVING (?count > 5)
}
""", initNs=NS)
print(task4.askAnswer)
# Which works fine in Blazegraph and is a valid SPARQL query will always provide false in RDFLib, cause it uses HAVING. Instead you have to use a nested SELECT query like below, where you use FILTER instead of HAVING. Donald Trump has no pardons, so I have instead chosen Bill Clinton (which has 13 pardons) to check if the query works.
task4 = g.query("""
ASK{
SELECT ?count WHERE{{
SELECT (COUNT(?s) as ?count) WHERE{
?s :pardoned :true;
:president :Bill_Clinton .
}}
FILTER (?count > 5)
}
}
""", initNs=NS)
print(task4.askAnswer)
# Use a DESCRIBE query to create a new graph with information about Donald Trump. Print out the graph in Turtle format.
# By all accounts, it seems DESCRIBE queries are yet to be implemented in RDFLib, but they are attempting to implement it: https://github.com/RDFLib/rdflib/pull/2221 (Issue and proposed solution raised) & https://github.com/RDFLib/rdflib/commit/2325b4a81724c1ccee3a131067db4fbf9b4e2629 (Solution committed to RDFLib). This solution does not work. However, this proposed solution should work if DESCRIBE is implemented in RDFLib
# task5 = g.query("""
# DESCRIBE :Donald_Trump
# """, initNs=NS)
# print(task5.serialize())
# ----- SPARQLWrapper -----
namespace = "kb" #Default namespace
sparql = SPARQLWrapper("http://localhost:9999/blazegraph/namespace/"+ namespace + "/sparql") #Replace localhost:9999 with your URL
# The current dates are URIs, we would want to change them to Literals with datatype "date" for task 1 & 2
update_str = """
PREFIX ns1: <http://example.org#>
DELETE {
?s ns1:cp_date ?cp;
ns1:investigation_end ?end;
ns1:investigation_start ?start.
}
INSERT{
?s ns1:cp_date ?cpDate;
ns1:investigation_end ?endDate;
ns1:investigation_start ?startDate.
}
WHERE{
?s ns1:cp_date ?cp . #Date conviction was recieved
BIND (replace(str(?cp), str(ns1:), "") AS ?cpRemoved)
BIND (STRDT(STR(?cpRemoved), xsd:date) AS ?cpDate)
?s ns1:investigation_end ?end . #Investigation End
BIND (replace(str(?end), str(ns1:), "") AS ?endRemoved)
BIND (STRDT(STR(?endRemoved), xsd:date) AS ?endDate)
?s ns1:investigation_start ?start . #Investigation Start
BIND (replace(str(?start), str(ns1:), "") AS ?startRemoved)
BIND (STRDT(STR(?startRemoved), xsd:date) AS ?startDate)
}"""
sparql.setQuery(update_str)
sparql.setMethod(POST)
sparql.query()
# Ask whether there was an ongoing indictment on the date 1990-01-01.
sparql.setQuery("""
PREFIX ns1: <http://example.org#>
ASK {
SELECT ?end ?start
WHERE{
?s ns1:investigation_end ?end;
ns1:investigation_start ?start;
ns1:outcome ns1:indictment.
FILTER(?start <= "1990-01-01"^^xsd:date && ?end >= "1990-01-01"^^xsd:date)
}
}
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()
print(f"Are there any investigation on the 1990-01-01: {results['boolean']}")
# List ongoing indictments on that date 1990-01-01.
sparql.setQuery("""
PREFIX ns1: <http://example.org#>
SELECT ?s
WHERE{
?s ns1:investigation_end ?end;
ns1:investigation_start ?start;
ns1:outcome ns1:indictment.
FILTER(?start <= "1990-01-01"^^xsd:date && ?end >= "1990-01-01"^^xsd:date)
}
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()
print("The ongoing investigations on the 1990-01-01 are:")
for result in results["results"]["bindings"]:
print(result["s"]["value"])
# Describe investigation number 100 (muellerkg:investigation_100).
sparql.setQuery("""
PREFIX ns1: <http://example.org#>
DESCRIBE ns1:investigation_100
""")
sparql.setReturnFormat(TURTLE)
results = sparql.query().convert()
print(results.serialize())
# Print out a list of all the types used in your graph.
sparql.setQuery("""
PREFIX ns1: <http://example.org#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT DISTINCT ?types
WHERE{
?s rdf:type ?types .
}
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()
rdf_Types = []
for result in results["results"]["bindings"]:
rdf_Types.append(result["types"]["value"])
print(rdf_Types)
# Update the graph to that every resource that is an object in a muellerkg:investigation triple has the rdf:type muellerkg:Investigation.
update_str = """
PREFIX ns1: <http://example.org#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
INSERT{
?invest rdf:type ns1:Investigation .
}
WHERE{
?s ns1:investigation ?invest .
}"""
sparql.setQuery(update_str)
sparql.setMethod(POST)
sparql.query()
#To Test
sparql.setQuery("""
prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX ns1: <http://example.org#>
ASK{
ns1:watergate rdf:type ns1:Investigation.
}
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()
print(results['boolean'])
# Update the graph to that every resource that is an object in a muellerkg:person triple has the rdf:type muellerkg:IndictedPerson.
update_str = """
PREFIX ns1: <http://example.org#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
INSERT{
?person rdf:type ns1:IndictedPerson .
}
WHERE{
?s ns1:person ?person .
}"""
sparql.setQuery(update_str)
sparql.setMethod(POST)
sparql.query()
#To test, run the query in the above task, replacing the ask query with e.g. ns1:Deborah_Gore_Dean rdf:type ns1:IndictedPerson
# Update the graph so all the investigation nodes (such as muellerkg:watergate) become the subject in a dc:title triple with the corresponding string (watergate) as the literal.
update_str = """
PREFIX ns1: <http://example.org#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX dc: <http://purl.org/dc/elements/1.1/>
INSERT{
?invest dc:title ?investString.
}
WHERE{
?s ns1:investigation ?invest .
BIND (replace(str(?invest), str(ns1:), "") AS ?investString)
}"""
sparql.setQuery(update_str)
sparql.setMethod(POST)
sparql.query()
#Same test as above, replace it with e.g. ns1:watergate dc:title "watergate"
# Print out a sorted list of all the indicted persons represented in your graph.
sparql.setQuery("""
PREFIX ns1: <http://example.org#>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?name
WHERE{
?s ns1:person ?name;
ns1:outcome ns1:indictment.
}
ORDER BY ?name
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()
names = []
for result in results["results"]["bindings"]:
names.append(result["name"]["value"])
print(names)
# Print out the minimum, average and maximum indictment days for all the indictments in the graph.
sparql.setQuery("""
prefix xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX ns1: <http://example.org#>
SELECT (AVG(?daysRemoved) as ?avg) (MAX(?daysRemoved) as ?max) (MIN(?daysRemoved) as ?min) WHERE{
?s ns1:indictment_days ?days;
ns1:outcome ns1:indictment.
BIND (replace(str(?days), str(ns1:), "") AS ?daysR)
BIND (STRDT(STR(?daysR), xsd:float) AS ?daysRemoved)
}
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()
for result in results["results"]["bindings"]:
print(f'The longest an investigation lasted was: {result["max"]["value"]}')
print(f'The shortest an investigation lasted was: {result["min"]["value"]}')
print(f'The average investigation lasted: {result["avg"]["value"]}')
# Print out the minimum, average and maximum indictment days for all the indictments in the graph per investigation.
sparql.setQuery("""
prefix xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX ns1: <http://example.org#>
SELECT ?investigation (AVG(?daysRemoved) as ?avg) (MAX(?daysRemoved) as ?max) (MIN(?daysRemoved) as ?min) WHERE{
?s ns1:indictment_days ?days;
ns1:outcome ns1:indictment;
ns1:investigation ?investigation.
BIND (replace(str(?days), str(ns1:), "") AS ?daysR)
BIND (STRDT(STR(?daysR), xsd:float) AS ?daysRemoved)
}
GROUP BY ?investigation
""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()
for result in results["results"]["bindings"]:
print(f'{result["investigation"]["value"]} - min: {result["min"]["value"]}, max: {result["max"]["value"]}, avg: {result["avg"]["value"]}')CSV To RDF (Lab 5)
#Imports
import re
from pandas import *
from numpy import nan
from rdflib import Graph, Namespace, URIRef, Literal, RDF, XSD, FOAF
from spotlight import SpotlightException, annotate
SERVER = "https://api.dbpedia-spotlight.org/en/annotate"
# Test around with the confidence, and see how many names changes depending on the confidence. However, be aware that anything lower than this (0.83) it will replace James W. McCord and other names that includes James with LeBron James
CONFIDENCE = 0.83
def annotate_entity(entity, filters={'types': 'DBpedia:Person'}):
annotations = []
try:
annotations = annotate(address=SERVER, text=entity, confidence=CONFIDENCE, filters=filters)
except SpotlightException as e:
print(e)
return annotations
g = Graph()
ex = Namespace("http://example.org/")
g.bind("ex", ex)
#Pandas' read_csv function to load russia-investigation.csv
df = read_csv("russia-investigation.csv")
#Replaces all instances of nan to None type with numpy's nan
df = df.replace(nan, None)
#Function that prepares the values to be added to the graph as a URI or Literal
def prepareValue(row):
if row == None: #none type
value = Literal(row)
elif isinstance(row, str) and re.match(r'\d{4}-\d{2}-\d{2}', row): #date
value = Literal(row, datatype=XSD.date)
elif isinstance(row, bool): #boolean value (true / false)
value = Literal(row, datatype=XSD.boolean)
elif isinstance(row, int): #integer
value = Literal(row, datatype=XSD.integer)
elif isinstance(row, str): #string
value = URIRef(ex + row.replace('"', '').replace(" ", "_").replace(",","").replace("-", "_"))
elif isinstance(row, float): #float
value = Literal(row, datatype=XSD.float)
return value
#Convert the non-semantic CSV dataset into a semantic RDF
def csv_to_rdf(df):
for index, row in df.iterrows():
id = URIRef(ex + "Investigation_" + str(index))
investigation = prepareValue(row["investigation"])
investigation_start = prepareValue(row["investigation-start"])
investigation_end = prepareValue(row["investigation-end"])
investigation_days = prepareValue(row["investigation-days"])
indictment_days = prepareValue(row["indictment-days "])
cp_date = prepareValue(row["cp-date"])
cp_days = prepareValue(row["cp-days"])
overturned = prepareValue(row["overturned"])
pardoned = prepareValue(row["pardoned"])
american = prepareValue(row["american"])
outcome = prepareValue(row["type"])
name_ex = prepareValue(row["name"])
president_ex = prepareValue(row["president"])
#Spotlight Search
name = annotate_entity(str(row['name']))
# Removing the period as some presidents won't be found with it
president = annotate_entity(str(row['president']).replace(".", ""))
#Adds the tripples to the graph
g.add((id, RDF.type, ex.Investigation))
g.add((id, ex.investigation, investigation))
g.add((id, ex.investigation_start, investigation_start))
g.add((id, ex.investigation_end, investigation_end))
g.add((id, ex.investigation_days, investigation_days))
g.add((id, ex.indictment_days, indictment_days))
g.add((id, ex.cp_date, cp_date))
g.add((id, ex.cp_days, cp_days))
g.add((id, ex.overturned, overturned))
g.add((id, ex.pardoned, pardoned))
g.add((id, ex.american, american))
g.add((id, ex.outcome, outcome))
#Spotlight search
#Name
try:
g.add((id, ex.person, URIRef(name[0]["URI"])))
except:
g.add((id, ex.person, name_ex))
#President
try:
g.add((id, ex.president, URIRef(president[0]["URI"])))
except:
g.add((id, ex.president, president_ex))
csv_to_rdf(df)
print(g.serialize())SHACL (Lab 6)
from pyshacl import validate
from rdflib import Graph
data_graph = Graph()
# parses the Turtle examples from the lab
data_graph.parse("data_graph.ttl")
# Remember to test you need to change the rules so they conflict with the data graph (or vice versa). For example, change "exactly one name" to have exactly two, and see the output
shape_graph = """
@prefix ex: <http://example.org/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix sh: <http://www.w3.org/ns/shacl#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
ex:LabTasks_Shape
a sh:NodeShape ;
sh:targetClass ex:PersonUnderInvestigation ;
sh:property [
sh:path foaf:name ;
sh:minCount 1 ; #Every person under investigation has exactly one name.
sh:maxCount 1 ; #Every person under investigation has exactly one name.
sh:datatype rdf:langString ; #All person names must be language-tagged
] ;
sh:property [
sh:path ex:chargedWith ;
sh:nodeKind sh:IRI ; #The object of a charged with property must be a URI.
sh:class ex:Offense ; #The object of a charged with property must be an offense.
] .
# --- If you have more time tasks ---
ex:MoreTime_Shape rdf:type sh:NodeShape;
sh:targetClass ex:Indictment;
# The only allowed values for ex:american are true, false or unknown.
sh:property [
sh:path ex:american;
sh:pattern "(true|false|unknown)" ;
] ;
# The value of a property that counts days must be an integer.
sh:property [
sh:path ex:indictment_days;
sh:datatype xsd:integer;
] ;
sh:property [
sh:path ex:investigation_days;
sh:datatype xsd:integer;
] ;
# The value of a property that indicates a start date must be xsd:date.
sh:property [
sh:path ex:investigation_start;
sh:datatype xsd:date;
] ;
# The value of a property that indicates an end date must be xsd:date or unknown (tip: you can use sh:or (...) ).
sh:property [
sh:path ex:investigation_end;
sh:or (
[ sh:datatype xsd:date ]
[ sh:hasValue "unknown" ]
)] ;
# Every indictment must have exactly one FOAF name for the investigated person.
sh:property [
sh:path foaf:name;
sh:minCount 1;
sh:maxCount 1;
] ;
# Every indictment must have exactly one investigated person property, and that person must have the type ex:PersonUnderInvestigation.
sh:property [
sh:path ex:investigatedPerson ;
sh:minCount 1 ;
sh:maxCount 1 ;
sh:class ex:PersonUnderInvestigation ;
sh:nodeKind sh:IRI ;
] ;
# No URI-s can contain hyphens ('-').
sh:property [
sh:path ex:outcome ;
sh:nodeKind sh:IRI ;
sh:pattern "^[^-]*$" ;
] ;
# Presidents must be identified with URIs.
sh:property [
sh:path ex:president ;
sh:class ex:President ;
sh:nodeKind sh:IRI ;
] .
"""
shacl_graph = Graph()
# parses the contents of a shape_graph made in the tasks
shacl_graph.parse(data=shape_graph)
# uses pySHACL's validate method to apply the shape_graph constraints to the data_graph
results = validate(
data_graph,
shacl_graph=shacl_graph,
inference='both'
)
# prints out the validation result
boolean_value, results_graph, results_text = results
# print(boolean_value)
print(results_graph.serialize(format='ttl'))
# print(results_text)
#Write a SPARQL query to print out each distinct sh:resultMessage in the results_graph
distinct_messages = """
PREFIX sh: <http://www.w3.org/ns/shacl#>
SELECT DISTINCT ?message WHERE {
[] sh:result ?errorBlankNode.
?errorBlankNode sh:resultMessage ?message.
# Alternativ and cleaner solution, look at https://www.w3.org/TR/sparql11-query/#pp-language (9.1 Property Path Syntax)
# [] sh:result / sh:resultMessage ?message .
}
"""
messages = results_graph.query(distinct_messages)
for row in messages:
print(row.message)
#each sh:resultMessage in the results_graph once, along with the number of times that message has been repeated in the results
count_messages = """
PREFIX sh: <http://www.w3.org/ns/shacl#>
SELECT ?message (COUNT(?node) AS ?num_messages) WHERE {
[] sh:result ?errorBlankNode .
?errorBlankNode sh:resultMessage ?message ;
sh:focusNode ?node .
}
GROUP BY ?message
ORDER BY DESC(?count) ?message
"""
messages = results_graph.query(count_messages)
for row in messages:
print(f"COUNT: {row.num_messages} | MESSAGE: {row.message}")RDFS (Lab 7)
import owlrl
from rdflib import Graph, RDF, Namespace, FOAF, RDFS
g = Graph()
ex = Namespace('http://example.org/')
g.bind("ex", ex)
g.bind("foaf", FOAF)
NS = {
'ex': ex,
'rdf': RDF,
'rdfs': RDFS,
'foaf': FOAF,
}
#Write a small function that computes the RDFS closure on your graph.
def flush():
owlrl.DeductiveClosure(owlrl.RDFS_Semantics).expand(g)
#Rick Gates was charged with money laundering and tax evasion.
g.add((ex.Rick_Gates, ex.chargedWith, ex.MoneyLaundering))
g.add((ex.Rick_Gates, ex.chargedWith, ex.TaxEvasion))
#When one thing that is charged with another thing,
g.add((ex.chargedWith, RDFS.domain, ex.PersonUnderInvestigation)) #the first thing is a person under investigation and
g.add((ex.chargedWith, RDFS.range, ex.Offense)) #the second thing is an offense.
#Write a SPARQL query that checks the RDF type(s) of Rick Gates and money laundering in your RDF graph.
print('Is Rick Gates a ex:PersonUnderInvestigation:', g.query('ASK {ex:Rick_Gates rdf:type ex:PersonUnderInvestigation}', initNs=NS).askAnswer)
print('Is Money Laundering a ex:Offense:', g.query('ASK {ex:MoneyLaundering rdf:type ex:Offense}', initNs=NS).askAnswer)
flush()
print('Is Rick Gates a ex:PersonUnderInvestigation:', g.query('ASK {ex:Rick_Gates rdf:type ex:PersonUnderInvestigation}', initNs=NS).askAnswer)
print('Is Money Laundering a ex:Offense:', g.query('ASK {ex:MoneyLaundering rdf:type ex:Offense}', initNs=NS).askAnswer)
#A person under investigation is a FOAF person
g.add((ex.PersonUnderInvestigation, RDFS.subClassOf, FOAF.Person))
print('Is Rick Gates a foaf:Person:', g.query('ASK {ex:Rick_Gates rdf:type foaf:Person}', initNs=NS).askAnswer)
flush()
print('Is Rick Gates a foaf:Person:', g.query('ASK {ex:Rick_Gates rdf:type foaf:Person}', initNs=NS).askAnswer)
#Paul Manafort was convicted for tax evasion.
g.add((ex.Paul_Manafort, ex.convictedFor, ex.TaxEvasion))
#the first thing is also charged with the second thing
g.add((ex.convictedFor, RDFS.subPropertyOf, ex.chargedWith))
print('Is Paul Manafort charged with Tax Evasion:', g.query('ASK {ex:Paul_Manafort ex:chargedWith ex:TaxEvasion}', initNs=NS).askAnswer)
flush()
print('Is Paul Manafort charged with Tax Evasion:', g.query('ASK {ex:Paul_Manafort ex:chargedWith ex:TaxEvasion}', initNs=NS).askAnswer)
print(g.serialize())OWL 1 (Lab 8)
from rdflib import Graph, RDFS, Namespace, RDF, FOAF, BNode, OWL, URIRef, Literal, XSD
from rdflib.collection import Collection
import owlrl
g = Graph()
ex = Namespace('http://example.org/')
schema = Namespace('http://schema.org/')
dbr = Namespace('https://dbpedia.org/page/')
g.bind("ex", ex)
# g.bind("schema", schema)
g.bind("foaf", FOAF)
# Donald Trump and Robert Mueller are two different persons.
g.add((ex.Donald_Trump, OWL.differentFrom, ex.Robert_Mueller))
# Actually, all the names mentioned in connection with the Mueller investigation refer to different people.
b1 = BNode()
b2 = BNode()
Collection(g, b2, [ex.Robert_Mueller, ex.Paul_Manafort, ex.Rick_Gates, ex.George_Papadopoulos, ex.Michael_Flynn, ex.Michael_Cohen, ex.Roger_Stone, ex.Donald_Trump])
g.add((b1, RDF.type, OWL.AllDifferent))
g.add((b1, OWL.distinctMembers, b2))
# All these people are foaf:Persons as well as schema:Persons
g.add((FOAF.Person, OWL.equivalentClass, schema.Person))
# Tax evation is a kind of bank and tax fraud.
g.add((ex.TaxEvation, RDFS.subClassOf, ex.BankFraud))
g.add((ex.TaxEvation, RDFS.subClassOf, ex.TaxFraud))
# The Donald Trump involved in the Mueller investigation is dbpedia:Donald_Trump and not dbpedia:Donald_Trump_Jr.
g.add((ex.Donald_Trump, OWL.sameAs, dbr.Donald_Trump))
g.add((ex.Donald_Trump, OWL.differentFrom, URIRef(dbr + "Donald_Trump_Jr.")))
# Congress, FBI and the Mueller investigation are foaf:Organizations.
g.add((ex.Congress, RDF.type, FOAF.Organization))
g.add((ex.FBI, RDF.type, FOAF.Organization))
g.add((ex.Mueller_Investigation, RDF.type, FOAF.Organization))
# Nothing can be both a person and an organization.
g.add((FOAF.Person, OWL.disjointWith, FOAF.Organization))
# Leading an organization is a way of being involved in an organization.
g.add((ex.leading, RDFS.subPropertyOf, ex.involved))
# Being a campaign manager or an advisor for is a way of supporting someone.
g.add((ex.campaignManagerTo, RDFS.subPropertyOf, ex.supports))
g.add((ex.advisorTo, RDFS.subPropertyOf, ex.supports))
# Donald Trump is a politician and a Republican.
g.add((ex.Donald_Trump, RDF.type, ex.Politician))
g.add((ex.Donald_Trump, RDF.type, ex.Republican))
# A Republican politician is both a politician and a Republican.
g.add((ex.RepublicanPolitician, RDFS.subClassOf, ex.Politician))
g.add((ex.RepublicanPolitician, RDFS.subClassOf, ex.Republican))
#hasBusinessPartner
g.add((ex.Paul_Manafort, ex.hasBusinessPartner, ex.Rick_Gates))
g.add((ex.hasBusinessPartner, RDF.type, OWL.SymmetricProperty))
g.add((ex.hasBusinessPartner, RDF.type, OWL.IrreflexiveProperty))
#adviserTo
g.add((ex.Michael_Flynn, ex.adviserTo, ex.Donald_Trump))
g.add((ex.adviserTo, RDF.type, OWL.IrreflexiveProperty))
# Not necessarily asymmetric as it's not a given that they couldn't be advisors to each other
#wasLyingTo
g.add((ex.Rick_Gates_Lying, ex.wasLyingTo, ex.FBI))
g.add((ex.wasLyingTo, RDF.type, OWL.IrreflexiveProperty))
# Not asymmetric as the subject and object could lie to each other; also in this context, the FBI can lie to you
#presidentOf
g.add((ex.Donald_Trump, ex.presidentOf, ex.USA))
g.add((ex.presidentOf, RDF.type, OWL.AsymmetricProperty))
g.add((ex.presidentOf, RDF.type, OWL.IrreflexiveProperty))
g.add((ex.presidentOf, RDF.type, OWL.FunctionalProperty)) #can only be president of one country
#not inversefunctionalproperty as Bosnia has 3 presidents https://www.culturalworld.org/do-any-countries-have-more-than-one-president.htm
#hasPresident
g.add((ex.USA, ex.hasPresident, ex.Donald_Trump))
g.add((ex.hasPresident, RDF.type, OWL.AsymmetricProperty))
g.add((ex.hasPresident, RDF.type, OWL.IrreflexiveProperty))
g.add((ex.hasPresident, RDF.type, OWL.InverseFunctionalProperty)) #countries do not share their president with another
#not functionalproperty as a country (Bosnia) can have more than one president
#Closure
owlrl.DeductiveClosure(owlrl.OWLRL_Semantics).expand(g)
#Serialization
print(g.serialize(format="ttl"))
# g.serialize("lab8.xml", format="xml") #serializes to XML fileOWL 2 (Lab 9)
NOTE: This is an OWL Protégé file
@prefix : <http://www.semanticweb.org/bruker/ontologies/2023/2/InvestigationOntology#> .
@prefix dc: <http://purl.org/dc/terms#> .
@prefix io: <http://www.semanticweb.org/bruker/ontologies/2023/2/InvestigationOntology#> .
@prefix dbr: <http://dbpedia.org/resource/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix xml: <http://www.w3.org/XML/1998/namespace> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@base <http://www.semanticweb.org/bruker/ontologies/2023/2/InvestigationOntology#> .
<http://www.semanticweb.org/bruker/ontologies/2023/2/InvestigationOntology> rdf:type owl:Ontology .
#################################################################
# Object Properties
#################################################################
### http://www.semanticweb.org/bruker/ontologies/2023/2/InvestigationOntology#hasLeader
io:hasLeader rdf:type owl:ObjectProperty ;
rdfs:subPropertyOf io:involved ;
owl:inverseOf io:leading .
### http://www.semanticweb.org/bruker/ontologies/2023/2/InvestigationOntology#indictedIn
io:indictedIn rdf:type owl:ObjectProperty ;
rdfs:subPropertyOf io:involvedIn ;
rdfs:domain io:InvestigatedPerson ;
rdfs:range io:Investigation .
### http://www.semanticweb.org/bruker/ontologies/2023/2/InvestigationOntology#investigating
io:investigating rdf:type owl:ObjectProperty ;
rdfs:subPropertyOf io:involvedIn ;
rdfs:domain io:Investigator ;
rdfs:range io:Investigation .
### http://www.semanticweb.org/bruker/ontologies/2023/2/InvestigationOntology#involved
io:involved rdf:type owl:ObjectProperty ;
owl:inverseOf io:involvedIn ;
rdfs:domain io:Investigation ;
rdfs:range foaf:Person .
### http://www.semanticweb.org/bruker/ontologies/2023/2/InvestigationOntology#involvedIn
io:involvedIn rdf:type owl:ObjectProperty ;
rdfs:domain foaf:Person ;
rdfs:range io:Investigation .
### http://www.semanticweb.org/bruker/ontologies/2023/2/InvestigationOntology#leading
io:leading rdf:type owl:ObjectProperty ;
rdfs:subPropertyOf io:investigating ;
rdfs:domain io:InvestigationLeader ;
rdfs:range io:Investigation .
### http://www.semanticweb.org/bruker/ontologies/2023/2/InvestigationOntology#teamMembers
io:teamMembers rdf:type owl:ObjectProperty ;
rdfs:subPropertyOf io:involved ;
rdfs:domain io:Investigation .
#################################################################
# Data properties
#################################################################
### http://purl.org/dc/elements/1.1/description
<http://purl.org/dc/elements/1.1/description> rdf:type owl:DatatypeProperty ;
rdfs:domain io:Investigation ;
rdfs:range xsd:string .
### http://www.w3.org/ns/prov#endedAtTime
prov:endedAtTime rdf:type owl:DatatypeProperty ,
owl:FunctionalProperty ;
rdfs:domain io:Investigation ;
rdfs:range xsd:dateTime .
### http://www.w3.org/ns/prov#startedAtTime
prov:startedAtTime rdf:type owl:DatatypeProperty ,
owl:FunctionalProperty ;
rdfs:domain io:Investigation ;
rdfs:range xsd:dateTime .
### http://xmlns.com/foaf/0.1/name
foaf:name rdf:type owl:DatatypeProperty ;
rdfs:domain foaf:Person ;
rdfs:range xsd:string .
### http://xmlns.com/foaf/0.1/title
foaf:title rdf:type owl:DatatypeProperty ;
rdfs:domain io:Investigation ;
rdfs:range xsd:string .
#################################################################
# Classes
#################################################################
### http://www.semanticweb.org/bruker/ontologies/2023/2/InvestigationOntology#InvestigatedPerson
io:InvestigatedPerson rdf:type owl:Class ;
rdfs:subClassOf io:Person ;
owl:disjointWith io:Investigator .
### http://www.semanticweb.org/bruker/ontologies/2023/2/InvestigationOntology#Investigation
io:Investigation rdf:type owl:Class .
### http://www.semanticweb.org/bruker/ontologies/2023/2/InvestigationOntology#InvestigationLeader
io:InvestigationLeader rdf:type owl:Class ;
rdfs:subClassOf io:Investigator .
### http://www.semanticweb.org/bruker/ontologies/2023/2/InvestigationOntology#Investigator
io:Investigator rdf:type owl:Class ;
rdfs:subClassOf io:Person .
### http://www.semanticweb.org/bruker/ontologies/2023/2/InvestigationOntology#Person
io:Person rdf:type owl:Class ;
rdfs:subClassOf foaf:Person .
### http://xmlns.com/foaf/0.1/Person
foaf:Person rdf:type owl:Class .
#################################################################
# Individuals
#################################################################
### http://dbpedia.org/resource/Donald_Trump
dbr:Donald_Trump rdf:type owl:NamedIndividual ;
io:involvedIn <http://dbpedia.org/resource/Special_Counsel_investigation_(2017–2019)> ;
foaf:name "Donald Trump" .
### http://dbpedia.org/resource/Elizabeth_Prelogar
dbr:Elizabeth_Prelogar rdf:type owl:NamedIndividual ;
io:investigating <http://dbpedia.org/resource/Special_Counsel_investigation_(2017–2019)> ;
foaf:name "Elizabeth Prelogar" .
### http://dbpedia.org/resource/Michael_Flynn
dbr:Michael_Flynn rdf:type owl:NamedIndividual ;
io:indictedIn <http://dbpedia.org/resource/Special_Counsel_investigation_(2017–2019)> ;
foaf:name "Michael Flynn" .
### http://dbpedia.org/resource/Paul_Manafort
dbr:Paul_Manafort rdf:type owl:NamedIndividual ;
io:indictedIn <http://dbpedia.org/resource/Special_Counsel_investigation_(2017–2019)> ;
foaf:name "Paul Manafort" .
### http://dbpedia.org/resource/Robert_Mueller
dbr:Robert_Mueller rdf:type owl:NamedIndividual ;
io:leading <http://dbpedia.org/resource/Special_Counsel_investigation_(2017–2019)> ;
foaf:name "Robert Mueller" .
### http://dbpedia.org/resource/Roger_Stone
dbr:Roger_Stone rdf:type owl:NamedIndividual ;
io:indictedIn <http://dbpedia.org/resource/Special_Counsel_investigation_(2017–2019)> ;
foaf:name "Roger Stone" .
### http://dbpedia.org/resource/Special_Counsel_investigation_(2017–2019)
<http://dbpedia.org/resource/Special_Counsel_investigation_(2017–2019)> rdf:type owl:NamedIndividual ;
io:hasLeader dbr:Robert_Mueller ;
io:involved dbr:Donald_Trump ,
dbr:Michael_Flynn ,
dbr:Paul_Manafort ,
dbr:Roger_Stone ;
io:teamMembers dbr:Elizabeth_Prelogar ,
dbr:Robert_Mueller ;
foaf:title "Mueller Investigation" .
#################################################################
# General axioms
#################################################################
[ rdf:type owl:AllDifferent ;
owl:distinctMembers ( dbr:Donald_Trump
dbr:Elizabeth_Prelogar
dbr:Michael_Flynn
dbr:Paul_Manafort
dbr:Robert_Mueller
dbr:Roger_Stone
)
] .
### Generated by the OWL API (version 4.5.25.2023-02-15T19:15:49Z) https://github.com/owlcs/owlapi