def movies_with_american_actors():
graph = KnowledgeGraph(graph_name='dbpedia')
dataset1 = graph.feature_domain_range('dbpp:starring', 'movie1', 'actor')\
.expand('actor', [('dbpp:birthPlace', 'actor_country1'), ('rdfs:label', 'actor_name1')])\
.expand('movie1', [('rdfs:label', 'movie_name1'), ('dcterms:subject', 'subject1'),
('dbpp:country', 'movie_country1'), ('dbpp:genre', 'genre1', True)])
# 26928 Rows. -- 4273 msec.
american_actors = dataset1.filter({'actor_country1': ['regex(str(?actor_country1), "USA")']})
# 1606 Rows. -- 7659 msec.
dataset2 = graph.feature_domain_range('dbpp:starring', 'movie2', 'actor')\
.expand('actor', [('dbpp:birthPlace', 'actor_country2'), ('rdfs:label', 'actor_name2')])\
.expand('movie2', [('rdfs:label', 'movie_name2'), ('dcterms:subject', 'subject2'),
('dbpp:country', 'movie_country2'), ('dbpp:genre', 'genre2', True)])
prolific_actors = dataset2.group_by(['actor'])\
.count('movie2', 'movie_count2', unique=True).filter({'movie_count2': ['>= 200']})
#663,769 Rows. -- 76704 msec.
movies = american_actors.join(prolific_actors, join_col_name1='actor', join_type=JoinType.OuterJoin)\
# .join(dataset, join_col_name1='actor')
#.select_cols(['movie_name', 'actor_name', 'genre'])
sparql_query = movies.to_sparql()
print(sparql_query)
def books_with_authors_cache():
graph = KnowledgeGraph(graph_name='dbpedia')
dataset = graph.feature_domain_range('dbpp:author', 'book', 'author')\
.expand('author', [('dbpp:birthPlace', 'author_country'),(' dbpp:education','education')])\
.expand('book', [('rdfs:label', 'work_name'),('dbpp:country','country', True),('dcterms:subject', 'subject'),
('dbpp:publisher','publisher', True)])\
.cache()
american_authors = dataset.filter(
{'author_country': ['regex(str(?author_country), "USA")']})
famous_authors = dataset.group_by(['author'])\
.count('book', 'book_count', unique=True).filter({'book_count': ['>= 2']})
books = american_authors.join(famous_authors,
join_col_name1='author',
join_type=JoinType.OuterJoin)
print(books.to_sparql())
def movies_with_american_actors():
start = time.time()
graph = KnowledgeGraph(graph_uri='http://dbpedia.org',
prefixes={
'dcterms': 'http://purl.org/dc/terms/',
'rdfs': 'http://www.w3.org/2000/01/rdf-schema#',
'dbpprop': 'http://dbpedia.org/property/',
'dbpr': 'http://dbpedia.org/resource/',
'dbpo': 'http://dbpedia.org/ontology/'
})
dataset = graph.feature_domain_range('dbpprop:starring', domain_col_name='film', range_col_name='actor')\
.expand('actor', [RDFPredicate('dbpprop:birthPlace', 'actor_country'), RDFPredicate('rdfs:label', 'actor_name')])\
.expand('film', [RDFPredicate('rdfs:label', 'film_name'), RDFPredicate('dcterms:subject', 'subject'),
RDFPredicate('dbpprop:country', 'film_country'), RDFPredicate('dbpo:genre', 'genre', optional=True)])\
.cache()
# 26928 Rows. -- 4273 msec.
american_actors = dataset.filter(
{'actor_country': ['regex(str(?actor_country), "USA")']})
# 1606 Rows. -- 7659 msec.
prolific_actors = dataset.group_by(['actor'])\
.count('film', 'film_count', unique=True).filter({'film_count': ['>= 20']})
#663,769 Rows. -- 76704 msec.
films = american_actors.join(prolific_actors, join_col_name1='actor', join_type=JoinType.OuterJoin)\
.join(dataset, join_col_name1='actor')
#.select_cols(['film_name', 'actor_name', 'genre'])
sparql_query = films.to_sparql()
print(sparql_query)
endpoint = 'http://10.161.202.101:8890/sparql/'
output_format = HttpClientDataFormat.PANDAS_DF
client = HttpClient(endpoint_url=endpoint, return_format=output_format)
# [663769 rows x 8 columns]
df = films.execute(client, return_format=output_format)
print("duration = {} sec".format(time.time() - start))
print(df)
def movies_with_american_actors_optional():
graph = KnowledgeGraph(graph_uri='http://dbpedia.org',
prefixes={'dcterms': 'http://purl.org/dc/terms/',
'rdfs': 'http://www.w3.org/2000/01/rdf-schema#',
'dbpprop': 'http://dbpedia.org/property/',
'dbpr': 'http://dbpedia.org/resource/'})
dataset = graph.feature_domain_range('dbpprop:starring', domain_col_name='movie', range_col_name='actor')\
.expand('actor', [
RDFPredicate('dbpprop:birthPlace', 'actor_country', optional=True),
RDFPredicate('rdfs:label', 'actor_name', optional=True)])\
.expand('movie', [
RDFPredicate('rdfs:label', 'movie_name', optional=True),
RDFPredicate('dcterms:subject', 'subject', optional=True),
RDFPredicate('dbpprop:country', 'movie_country', optional=True)])\
.cache()
# 26928 Rows. -- 4273 msec.
american_actors = dataset.filter({'actor_country': ['regex(str(?actor_country), "USA")']})
# 1606 Rows. -- 7659 msec.
prolific_actors = dataset.group_by(['actor'])\
.count('movie', 'movie_count', unique=True).filter({'movie_count': ['>= 20', '<=30']})
# 663769 Rows. -- 76511 msec.
movies = american_actors.join(prolific_actors, join_col_name1='actor', join_type=JoinType.OuterJoin)\
.join(dataset, join_col_name1='actor')
sparql_query = movies.to_sparql()
print(sparql_query)
endpoint = 'http://10.161.202.101:8890/sparql/'
output_format = HttpClientDataFormat.PANDAS_DF
client = HttpClient(endpoint_url=endpoint, return_format=output_format)
df = dataset.execute(client, return_format=output_format)
print(df)
def kge():
graph = KnowledgeGraph(graph_uri='http://dblp.13s.de/')
triples = graph.feature_domain_range("pred", domain_col_name='sub', range_col_name='obj')\
.filter({'obj': ['isIRI(?obj)']})
print(triples.to_sparql())
from rdfframes.knowledge_graph import KnowledgeGraph
from rdfframes.utils.constants import JoinType
from rdfframes.client.http_client import HttpClientDataFormat, HttpClient
from rdfframes.dataset.rdfpredicate import PredicateDirection
graph = KnowledgeGraph(graph_name='dbpedia')
endpoint = 'http://10.161.202.101:8890/sparql/'
output_format = HttpClientDataFormat.PANDAS_DF
client = HttpClient(endpoint_url=endpoint, return_format=output_format)
movies = graph.feature_domain_range('dbpp:starring',
domain_col_name='movie',
range_col_name='actor')
american_actors = movies.expand('actor', [('dbpp:birthPlace', 'actor_country')])\
.filter({'actor_country': ['=dbpr:United_States']})
american_prolific = american_actors.group_by(['actor'])\
.count('movie', 'movie_count', unique=True).filter({'movie_count': ['>=50']})
movies = american_prolific.expand(
'actor', [('dbpp:starring', 'movie', False, PredicateDirection.INCOMING),
('dbpp:academyAward', 'award', True)])
print(movies.to_sparql())
"""
movies = graph.feature_domain_range('dbpp:starring', domain_col_name='movie', range_col_name='actor').cache()
big_american_name = movies.group_by(['actor'])\
.count('movie', 'movie_count', unique=True).filter({'movie_count': ['>=60']}) \
.select_cols(['actor', 'movie_count'])\
# Graph, client, and the SPARQL endpoint URI
graph = KnowledgeGraph(graph_uri='http://dbpedia.org',
prefixes= {'dcterms': 'http://purl.org/dc/terms/',
'rdfs': 'http://www.w3.org/2000/01/rdf-schema#',
'dbpprop': 'http://dbpedia.org/property/',
'dbpr': 'http://dbpedia.org/resource/'})
endpoint = 'http://10.161.202.101:8890/sparql/'
output_format = HttpClientDataFormat.PANDAS_DF
timeout = 12000
client = HttpClient(endpoint_url=endpoint, return_format=output_format)
# RDFFrames code for creating the dataframe
dataset = graph.feature_domain_range('dbpp:starring', 'movie', 'actor')\
.expand('actor', [('dbpp:birthPlace', 'actor_country'), ('rdfs:label', 'actor_name')])\
.expand('movie', [('rdfs:label', 'movie_name'),('dcterms:subject', 'subject'), ('dbpp:genre', 'genre', True)]).cache()
american_actors = dataset.filter({'actor_country': ['regex(str(?actor_country), "USA")']})
prolific_actors = dataset.group_by(['actor']) .count('movie', 'movie_count', unique=True).filter({'movie_count': ['>= 100']})
movies = american_actors.join(prolific_actors, join_col_name1='actor', join_type=JoinType.OuterJoin).join(dataset, join_col_name1='actor')\
.select_cols([ "actor_name","movie_name","actor_country","genre","subject"])
sparql_query = movies.to_sparql()
print(sparql_query)
# execution
df = movies.execute(client, return_format=output_format)
# Preprocessing and preparation
