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Here we will present suggested solutions after each lab. ''The page will be updated as the course progresses''
Here we will present suggested solutions after each lab. ''The page will be updated as the course progresses''


<!--
=Getting started (Lab 1)=
=Getting started (Lab 1)=


Line 334: Line 335:
       OPTIONAL{
       OPTIONAL{
         ?s ns1:pardoned ?pardon .
         ?s ns1:pardoned ?pardon .
         FILTER (?pardon = ns1:true)
         FILTER (?pardon = true)
       }
       }
}
}
Line 827: Line 828:


</syntaxhighlight>
</syntaxhighlight>
=Wikidata SPARQL (Lab 6)=
===Use a DESCRIBE query to retrieve some triples about your entity===
<syntaxhighlight lang="SPARQL">
DESCRIBE wd:Q42 LIMIT 100
</syntaxhighlight>
===Use a SELECT query to retrieve the first 100 triples about your entity===
<syntaxhighlight lang="SPARQL">
SELECT * WHERE {
  wd:Q42 ?p ?o .
} LIMIT 100
</syntaxhighlight>
===Write a local SELECT query that embeds a SERVICE query to retrieve the first 100 triples about your entity to your local machine===
<syntaxhighlight lang="SPARQL">
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT * WHERE {
    SERVICE <https://query.wikidata.org/bigdata/namespace/wdq/sparql> {
        SELECT * WHERE {
            wd:Q42 ?p ?o .
        } LIMIT 100
    }
}
</syntaxhighlight>
===Change the SELECT query to an INSERT query that adds the Wikidata triples your local repository===
<syntaxhighlight lang="SPARQL">
PREFIX wd: <http://www.wikidata.org/entity/>
INSERT {
    wd:Q42 ?p ?o .
} WHERE {
    SERVICE <https://query.wikidata.org/bigdata/namespace/wdq/sparql> {
        SELECT * WHERE {
            wd:Q42 ?p ?o .
        } LIMIT 100
    }
}
</syntaxhighlight>
===Use a FILTER statement to only SELECT primary triples in this sense.===
<syntaxhighlight lang="SPARQL">
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT * WHERE {
    wd:Q42 ?p ?o .
    FILTER (STRSTARTS(STR(?p), STR(wdt:)))
    FILTER (STRSTARTS(STR(?o), STR(wd:)))
} LIMIT 100
</syntaxhighlight>
===Use Wikidata's in-built SERVICE wikibase:label to get labels for all the object resources===
<syntaxhighlight lang="SPARQL">
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?p ?oLabel WHERE {
    wd:Q42 ?p ?o .
    FILTER (STRSTARTS(STR(?p), STR(wdt:)))
    FILTER (STRSTARTS(STR(?o), STR(wd:)))
    SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
} LIMIT 100
</syntaxhighlight>
===Edit your query (by relaxing the FILTER expression) so it also returns triples where the object has DATATYPE xsd:string.===
<syntaxhighlight lang="SPARQL">
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?p ?oLabel ?o WHERE {
    wd:Q42 ?p ?o .
    FILTER (STRSTARTS(STR(?p), STR(wdt:)))
    FILTER (
      STRSTARTS(STR(?o), STR(wd:)) ||  # comment out this whole line to see only string literals!
      DATATYPE(?o) = xsd:string
    )
    SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
} LIMIT 100
</syntaxhighlight>
===Relax the FILTER expression again so it also returns triples with these three predicates (rdfs:label, skos:altLabel and schema:description) ===
<syntaxhighlight lang="SPARQL">
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?p ?oLabel ?o WHERE {
    wd:Q42 ?p ?o .
    FILTER (
      (STRSTARTS(STR(?p), STR(wdt:)) &&  # comment out these three lines to see only fingerprint literals!
      STRSTARTS(STR(?o), STR(wd:)) || DATATYPE(?o) = xsd:string)
      ||
      (?p IN (rdfs:label, skos:altLabel, schema:description) &&
      DATATYPE(?o) = rdf:langString && LANG(?o) = "en")
    )
    SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
} LIMIT 100
</syntaxhighlight>
===Try to restrict the FILTER expression again so that, when the predicate is rdfs:label, skos:altLabel and schema:description, the object must have LANG "en" ===
<syntaxhighlight lang="SPARQL">
PREFIX wikibase: <http://wikiba.se/ontology#>
PREFIX bd: <http://www.bigdata.com/rdf#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
PREFIX schema: <http://schema.org/>
SELECT * WHERE {
  SERVICE <https://query.wikidata.org/bigdata/namespace/wdq/sparql> {
    SELECT ?p ?oLabel ?o WHERE {
        wd:Q42 ?p ?o .
        FILTER (
          (STRSTARTS(STR(?p), STR(wdt:)) &&
          STRSTARTS(STR(?o), STR(wd:)) || DATATYPE(?o) = xsd:string)
          ||
          (?p IN (rdfs:label, skos:altLabel, schema:description) &&
          DATATYPE(?o) = rdf:langString && LANG(?o) = "en")
        )
        SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
    } LIMIT 100
  }
}
</syntaxhighlight>
===Change the SELECT query to an INSERT query that adds the Wikidata triples your local repository ===
<syntaxhighlight lang="SPARQL">
PREFIX wikibase: <http://wikiba.se/ontology#>
PREFIX bd: <http://www.bigdata.com/rdf#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
PREFIX schema: <http://schema.org/>
INSERT {
  wd:Q42 ?p ?o .
  ?o rdfs:label ?oLabel .
} WHERE {
  SERVICE <https://query.wikidata.org/bigdata/namespace/wdq/sparql> {
    SELECT ?p ?oLabel ?o WHERE {
        wd:Q42 ?p ?o .
        FILTER (
          (STRSTARTS(STR(?p), STR(wdt:)) &&
          STRSTARTS(STR(?o), STR(wd:)) || DATATYPE(?o) = xsd:string)
          ||
          (?p IN (rdfs:label, skos:altLabel, schema:description) &&
          DATATYPE(?o) = rdf:langString && LANG(?o) = "en")
        )
        SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
    } LIMIT 500
  }
}
</syntaxhighlight>
==If you have more time ==
===You must therefore REPLACE all wdt: prefixes of properties with wd: prefixes and BIND the new URI AS a new variable, for example ?pw. ===
<syntaxhighlight lang="SPARQL">
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?pwLabel ?oLabel WHERE {
    wd:Q42 ?p ?o .
    FILTER (STRSTARTS(STR(?p), STR(wdt:)))
    FILTER (STRSTARTS(STR(?o), STR(wd:)))
    BIND (IRI(REPLACE(STR(?p), STR(wdt:), STR(wd:))) AS ?pw)
    SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
} LIMIT 100
</syntaxhighlight>
===Now you can go back to the SELECT statement that returned primary triples with only resource objects (not literal objects or fingerprints). Extend it so it also includes primary triples "one step out", i.e., triples where the subjects are objects of triples involving your reference entity. ===
<syntaxhighlight lang="SPARQL">
PREFIX wikibase: <http://wikiba.se/ontology#>
PREFIX bd: <http://www.bigdata.com/rdf#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
PREFIX schema: <http://schema.org/>
INSERT {
  wd:Q42 ?p1 ?o1 .
  ?o1 rdfs:label ?o1Label .
  ?o1 ?p2 ?o2 .
  ?o2 rdfs:label ?o2Label .
} WHERE {
  SERVICE <https://query.wikidata.org/bigdata/namespace/wdq/sparql> {
    SELECT ?p1 ?o1Label ?o1 ?p2 ?o2Label ?o2 WHERE {
        wd:Q42 ?p1 ?o1 .
        ?o1 ?p2 ?o2 .
        FILTER (
          STRSTARTS(STR(?p1), STR(wdt:)) &&
          STRSTARTS(STR(?o1), STR(wd:)) &&
          STRSTARTS(STR(?p2), STR(wdt:)) &&
          STRSTARTS(STR(?o2), STR(wd:))
        )
        SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
    } LIMIT 500
  }
}
</syntaxhighlight>
=CSV to RDF (Lab 7)=
<syntaxhighlight lang="Python">
#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
# This function uses DBpedia Spotlight, which was not a part of the CSV lab this year. 
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 (ex infront) 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']))
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())
g.serialize("lab7.ttl", format="ttl")
</syntaxhighlight>
=JSON-LD (Lab 8)=
== Task 1) Basic JSON-LD ==
<syntaxhighlight lang="JSON-LD">
{
    "@context": {
        "@base": "http://example.org/",
        "edges": "http://example.org/triple",
        "start": "http://example.org/source",
        "rel": "http://exaxmple.org/predicate",
        "end": "http://example.org/object",
        "Person" : "http://example.org/Person",
        "birthday" : {
            "@id" : "http://example.org/birthday",
            "@type" : "xsd:date"
        },
        "nameEng" : {
            "@id" : "http://example.org/en/name",
            "@language" : "en"
        },
        "nameFr" : {
            "@id" : "http://example.org/fr/name",
            "@language" : "fr"
        },
        "nameCh" : {
            "@id" : "http://example.org/ch/name",
            "@language" : "ch"
        },
        "age" : {
            "@id" : "http://example.org/age",
            "@type" : "xsd:int"
        },
        "likes" : "http://example.org/games/likes",
        "haircolor" : "http://example.org/games/haircolor"
    },
    "@graph": [
        {
            "@id": "people/Jeremy",
            "@type": "Person",
            "birthday" : "1987.1.1",
            "nameEng" : "Jeremy",
            "age" : 26
        },
        {
            "@id": "people/Tom",
            "@type": "Person"
        },
        {
            "@id": "people/Ju",
            "@type": "Person",
            "birthday" : "2001.1.1",
            "nameCh" : "Ju",
            "age" : 22,
            "likes" : "bastketball"
        },
        {
            "@id": "people/Louis",
            "@type": "Person",
            "birthday" : "1978.1.1",
            "haircolor" : "Black",
            "nameFr" : "Louis",
            "age" : 45
        },
        {"edges" : [
        {
            "start" : "people/Jeremy",
            "rel" : "knows",
            "end" : "people/Tom"
        },
        {
            "start" : "people/Tom",
            "rel" : "knows",
            "end" : "people/Louis"
        },
        {
            "start" : "people/Louis",
            "rel" : "teaches",
            "end" : "people/Ju"
        },
        {
            "start" : "people/Ju",
            "rel" : "plays",
            "end" : "people/Jeremy"
        },
        {
            "start" : "people/Ju",
            "rel" : "plays",
            "end" : "people/Tom"
        }
        ]}
    ]
}
</syntaxhighlight>
== Task 2 & 3) Retrieving JSON-LD from ConceptNet / Programming JSON-LD in Python ==
<syntaxhighlight lang="Python">
import rdflib
CN_BASE = 'http://api.conceptnet.io/c/en/'
g = rdflib.Graph()
g.parse(CN_BASE+'indictment', format='json-ld')
# To download JSON object:
import json
import requests
json_obj = requests.get(CN_BASE+'indictment').json()
# To change the @context:
context = {
    "@base": "http://ex.org/",
    "edges": "http://ex.org/triple/",
    "start": "http://ex.org/s/",
    "rel": "http://ex.org/p/",
    "end": "http://ex.org/o/",
    "label": "http://ex.org/label"
}
json_obj['@context'] = context
json_str = json.dumps(json_obj)
g = rdflib.Graph()
g.parse(data=json_str, format='json-ld')
# To extract triples (here with labels):
r = g.query("""
        SELECT ?s ?sLabel ?p ?o ?oLabel WHERE {
            ?edge
                <http://ex.org/s/> ?s ;
                <http://ex.org/p/> ?p ;
                <http://ex.org/o/> ?o .
            ?s <http://ex.org/label> ?sLabel .
            ?o <http://ex.org/label> ?oLabel .
}
        """, initNs={'cn': CN_BASE})
print(r.serialize(format='txt').decode())
# Construct a new graph:
r = g.query("""
        CONSTRUCT {
            ?s ?p ?o .
            ?s <http://ex.org/label> ?sLabel .
            ?o <http://ex.org/label> ?oLabel .
        } WHERE {
            ?edge <http://ex.org/s/> ?s ;
                  <http://ex.org/p/> ?p ;
                  <http://ex.org/o/> ?o .
            ?s <http://ex.org/label> ?sLabel .
            ?o <http://ex.org/label> ?oLabel .
}
        """, initNs={'cn': CN_BASE})
print(r.graph.serialize(format='ttl'))
</syntaxhighlight>
=SHACL (Lab 9)=
<syntaxhighlight lang="Python">
from pyshacl import validate
from rdflib import Graph
data_graph = Graph()
# parses the Turtle example from the task
data_graph.parse("data_graph.ttl")
prefixes = """
@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#> .
"""
shape_graph = """
ex:PUI_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:User_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:minCount 1 ;
        sh:class ex:President ;
        sh:nodeKind sh:IRI ;
    ] .
"""
shacl_graph = Graph()
# parses the contents of a shape_graph you made in the previous task
shacl_graph.parse(data=prefixes+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 / 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 ?result .
    ?result 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("COUNT    MESSAGE")
    print(row.num_messages, "      ", row.message)
</syntaxhighlight>
=RDFS (Lab 10)=
<syntaxhighlight lang="Python">
import owlrl
from rdflib import Graph, RDF, Namespace, Literal, XSD, FOAF, RDFS
from rdflib.collection import Collection
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():
    engine = owlrl.RDFSClosure.RDFS_Semantics(g, False, False, False)
    engine.closure()
    engine.flush_stored_triples()
#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 (subject) is a person under investigation and
g.add((ex.chargedWith, RDFS.range, ex.Offense))  #the second thing (object) is an offense.
#Write a SPARQL query that checks the RDF type(s) of Rick Gates and money laundering in your RDF graph.
print(g.query('ASK {ex:Rick_Gates rdf:type ex:PersonUnderInvestigation}', initNs=NS).askAnswer)
print(g.query('ASK {ex:MoneyLaundering rdf:type ex:Offense}', initNs=NS).askAnswer)
flush()
print(g.query('ASK {ex:Rick_Gates rdf:type ex:PersonUnderInvestigation}', initNs=NS).askAnswer)
print(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(g.query('ASK {ex:Rick_Gates rdf:type foaf:Person}', initNs=NS).askAnswer)
flush()
print(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))
flush()
print(g.query('ASK {ex:Paul_Manafort ex:chargedWith ex:TaxEvasion}', initNs=NS).askAnswer)
print(g.serialize())
</syntaxhighlight>
=OWL 1 (Lab 11)=
<syntaxhighlight lang="Python">
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 file
</syntaxhighlight>
=OWL 2 (Lab 12)=
<syntaxhighlight lang="Python">
@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#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#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 .
#################################################################
#    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 ;
                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 ;
                  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 ;
                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 ;
                                                                        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
</syntaxhighlight>
=Using Graph Embeddings (Lab 13)=
https://colab.research.google.com/drive/1WkRJUeUBVF5yVv7o0pOKfsd4pqG6369k
=Training Graph Embeddings (Lab 14)=
https://colab.research.google.com/drive/1jKpzlQ7gYTVzgphJsrK5iuMpFhkrY96q
-->

Latest revision as of 10:56, 20 January 2025

Here we will present suggested solutions after each lab. The page will be updated as the course progresses


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