Solution examples 2023: Difference between revisions

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'''For questions 55-60:'''
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*** SHACL examples - includes answers to the exam questions
*** SHACL examples - includes answers to the exam questions

Revision as of 12:07, 6 May 2024

Task 2: RDF and SHACL

Questions 51-54:
RDF: Add triples TBD


For questions 55-60: 

*** SHACL examples - includes answers to the exam questions

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . 
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . 
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> . 
@prefix owl: <http://www.w3.org/2002/07/owl#> . 
@prefix dc: <http://purl.org/dc/elements/1.1/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix sh: <http://www.w3.org/ns/shacl#> .
@prefix : <http://info216.uib.no/movies/> .

Questions 55 and 56: 

:DirectorShape a sh:NodeShape ;
    sh:targetClass :Director ;
    # A Director must have exactly one foaf:name of type xsd:string.
    sh:property [
        sh:path foaf:name ;
        sh:minCount 1 ;      # question 55
        sh:maxCount 1 ;      # question 55
        sh:type xsd:string   # question 56
    ] ;

Question 57: 

    # A Director must be the director of at least one Movie.
    sh:property [
        sh:path :director_of ;
        sh:minCount 1 ;
        sh:class :Movie
    ] .

Question 58: 

:ActorShape a sh:NodeShape ;
    sh:targetClass :Actor ;
    # If an actor is an actor in a resource, that resource must be a movie.
    sh:property [
        sh:path :actor_in ;
        sh:minCount 1 ;
        sh:class :Movie
    ] ;

Question 59: 

:ActorShape a sh:NodeShape ;
    sh:targetClass :Actor ;
    sh:property [  # this is informative - not mandatory part of answer
        sh:path :plays_role ;
        sh:class :Role ;
    ] ;
    # If an actor plays a role that is a role in some resource, that resource must be a movie.
    sh:property [
        sh:path ( :plays_role :role_in ) ;
        sh:qualifiedValueShape [ sh:path :actor_in ] ;
        sh:qualifiedMinCount 1 ;
    ] .

Question 60: 

:MovieShape a sh:NodeShape ;
    sh:targetClass :Movie ;
    # A movie must be directed by at least one director or acted in by at least one actor.
    sh:or (
        [ sh:property [
            sh:path [ sh:inversePath :actor_in ] ;
            sh:minCount 1 ;
        ] ]
        [ sh:property [
            sh:path [ sh:inversePath :director_of ] ;
            sh:minCount 1 ;
        ] ] 
    ) .




      • RDFS rules

A resource that is a director_of something is a director.

director_of rdfs:domain :Director .

A resource that something else is a director_of is a movie.

director_of rdfs:range :Movie .

The year of something has type xsd:year.

year rdfs:range xsd:year .

An actor is a Person.

Actor rdfs:subClassOf foaf:Person .

A director is a person.

Director rdfs:subClassOf foaf:Person .


      • OWL axioms

Nothing can be both a person and a movie.

Person owl:disjointWith :Movie .


Nothing can be more than one of a person, a role, or a movie.

[] a owl:DisjointClass ; 

   owl:disjointClasses ( :Person :Role :Movie ) .

Something that plays in at least one Movie is an Actor.

Actor rdfs:subClassOf [
   a owl:Restriction ;
   owl:onProperty :play_in ;
   owl:someValueFrom owl:Thing

]

A LeadActor is an Actor that plays at least one LeadRole.

LeadActor rdfs:subClassOf :Actor, [
   a owl:Restriction ;
   owl:onProperty :plays_role ;
   owl:someValueFrom :LeadRole .

] .


      • SPARQL queries

Count the number of movies that are represented in the graph.

SELECT (COUNT(?movie) AS ?count) WHERE { 

   ?movie rdf:type :Movie

}

List the titles and years of all movies.

SELECT ?title ?year WHERE { 

   ?movie rdf:type :Movie ;
       dc:title ?title ;
       dc:year ?year

}

List the titles and years of all movies since 2000.

SELECT ?title ?year WHERE { 

   ?movie rdf:type :Movie ;
       dc:title ?title ;
       dc:year ?year 
   FILTER (INTEGER(?year) >= 2000)

}

SELECT ?title ?year WHERE { 

   ?movie rdf:type :Movie ;
       dc:title ?title ;
       dc:year ?year 
   FILTER (?year >= "2000"^^xsd:year)

}

List the titles and years of all movies sorted first by year, then by name.

SELECT ?title ?year WHERE { 

   ?movie rdf:type :Movie ;
       dc:title ?title ;
       dc:year ?year

} ORDER BY ?year, ?name

Count the number of movies for each year with more than one movie.

SELECT ?year (COUNT(?movie) AS ?count) WHERE { 

   ?movie rdf:type :Movie ;
       dc:year ?year

} GROUP BY ?year HAVING ?count > 1

List the names of all persons that are both directors and actors.

SELECT ?name WHERE { 

   ?person (:plays_in & :director_of) / rdf:type :Movie ;
       foaf:name ?name

}

List the actor name and movie title for all lead roles.

SELECT ?name ?title WHERE { 

   ?role rdf:type :LeadRole ;
       ^:plays_role / foaf:name ?name ;
       :role_in / dc:title ?title

}

List all distinct pairs of actor names that have played lead roles in the same movies.

SELECT ?name1 ?name2 WHERE { 

   ?movie rdf:type :Movie ;
       ^:?role_in ?role1, ?role2 .
   ?role1 rdf:type :LeadRole ;
       ^:plays_role / foaf:name ?name1 .
   ?role2 rdf:type :LeadRole ;
       ^:plays_role / foaf:name ?name2 .
   FILTER (STR(?name1) < STR(?name2))

}


      • Examples related to the programming task

from owlrl import DeductiveClosure, OWLRL_Semantics import pandas as pd from pyshacl import validate from rdflib import Namespace, Graph, Literal, RDF, DC, FOAF, XSD


ONTOLOGY_FILE = './movie-ontology.ttl' SHACL_FILE = './movie-shacl.ttl' DIRECTOR_FILE = './movie-director-year.csv' LEAD_ROLE_FILE = './movie-actor-lead-role.csv' OTHER_ROLE_FILE = './movie-actor-other-role.csv'

BASE_URI = 'http://example.org/' MOVIE = Namespace(BASE_URI)


def add_movie_triples(g, row): 

   movie = row.to_dict()
   # example dict:
   # {'Movie': 'Pulp_Fiction', 'Director': 'Quentin_Tarantino', 'Year': 1994}
   movie_name = movie['Movie']
   director_name = movie['Director']
   movie_year = movie['Year']
   # update g with a set of triples that represent the movie and its director
   g.add((MOVIE[director_name], RDF.type, MOVIE.Director))
   g.add((MOVIE[director_name], FOAF.name, Literal(director_name)))
   g.add((MOVIE[director_name], MOVIE.director_of, MOVIE[movie_name]))
   g.add((MOVIE[movie_name], RDF.type, MOVIE.Movie))
   g.add((MOVIE[movie_name], DC.title, Literal(movie_name)))
   g.add((MOVIE[movie_name], MOVIE.year, Literal(movie_year, datatype=XSD.year)))


def add_lead_role_triples(g, row): 

   movie = row.to_dict()
   # example dict:
   # {'Movie': 'Pulp_Fiction', 'Director': 'Quentin_Tarantino', 'Year': 1994}
   movie_name = movie['Movie']
   actor_name = movie['Actor']
   role_name = movie_name+'-role-'+movie['LeadRole']
   # update g with a set of triples that represent the movie and its director
   g.add((MOVIE[actor_name], RDF.type, MOVIE.Actor))
   g.add((MOVIE[actor_name], FOAF.name, Literal(actor_name)))
   g.add((MOVIE[actor_name], MOVIE.actor_in, MOVIE[movie_name]))
   g.add((MOVIE[actor_name], MOVIE.plays_role, MOVIE[role_name]))
   g.add((MOVIE[role_name], RDF.type, MOVIE.LeadRole))
   g.add((MOVIE[role_name], FOAF.name, Literal(movie['LeadRole'])))
   g.add((MOVIE[role_name], MOVIE.role_in, MOVIE[movie_name]))
   g.add((MOVIE[movie_name], RDF.type, MOVIE.Movie))


def add_other_role_triples(g, row): 

   movie = row.to_dict()
   # example dict:
   # {'Movie': 'Pulp_Fiction', 'Director': 'Quentin_Tarantino', 'Year': 1994}
   movie_name = movie['Movie']
   actor_name = movie['Actor']
   role_name = movie_name+'-role-'+movie['Role']
   # update g with a set of triples that represent the movie and its director
   g.add((MOVIE[actor_name], RDF.type, MOVIE.Actor))
   g.add((MOVIE[actor_name], FOAF.name, Literal(actor_name)))
   g.add((MOVIE[actor_name], MOVIE.actor_in, MOVIE[movie_name]))
   g.add((MOVIE[actor_name], MOVIE.plays_role, MOVIE[role_name]))
   g.add((MOVIE[role_name], RDF.type, MOVIE.Role))
   g.add((MOVIE[role_name], FOAF.name, Literal(movie['Role'])))
   g.add((MOVIE[role_name], MOVIE.role_in, MOVIE[movie_name]))
   g.add((MOVIE[movie_name], RDF.type, MOVIE.Movie))


def load_movie_triples(g, fn): 

   df = pd.read_csv(fn)
   df.apply(lambda row: add_movie_triples(g, row), axis=1)


def load_lead_role_triples(g, fn): 

   df = pd.read_csv(fn)
   df.apply(lambda row: add_lead_role_triples(g, row), axis=1)


def load_other_role_triples(g, fn): 

   df = pd.read_csv(fn)
   df.apply(lambda row: add_other_role_triples(g, row), axis=1)


g = Graph() g.bind(, MOVIE) load_movie_triples(g, DIRECTOR_FILE) load_lead_role_triples(g, LEAD_ROLE_FILE) load_other_role_triples(g, OTHER_ROLE_FILE) print(g.serialize(format='ttl'))


sg = Graph() sg.parse(SHACL_FILE, format='ttl') r = validate(g, 

       shacl_graph=sg,
       # ont_graph=og,
       inference='rdfs'
   )

val, rg, rep = r print(rep)


g.parse(ONTOLOGY_FILE) DeductiveClosure(OWLRL_Semantics).expand(g)

print(g.serialize(format='ttl'))

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