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https://github.com/gsi-upm/sitc
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Add Beatles introduction
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lod/01_SPARQL_Introduction.ipynb
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1870
lod/01_SPARQL_Introduction.ipynb
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lod/SPARQL.ipynb
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lod/SPARQL.ipynb
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@ -1,49 +0,0 @@
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import sys
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from future.standard_library import install_aliases
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install_aliases()
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from urllib import request, parse
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from rdflib import Graph, term
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from lxml import etree
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if len(sys.argv) < 2:
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print('Usage: python {} <URL>'.format(sys.argv[0]))
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print('')
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print('Extract rdfa, microdata and json-ld annotations from a website')
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exit(1)
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url = sys.argv[1]
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g = Graph()
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g.parse(url, format='rdfa')
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g.parse(url, format='microdata')
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def sanitize_triple(t):
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"""Function to remove bad URIs from the graph that would otherwise
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make the serialization fail."""
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def sanitize_triple_item(item):
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if isinstance(item, term.URIRef) and '/' not in item:
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return term.URIRef(parse.quote(str(item)))
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return item
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return (sanitize_triple_item(t[0]),
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sanitize_triple_item(t[1]),
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sanitize_triple_item(t[2]))
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with request.urlopen(url) as response:
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# Get all json-ld objects embedded in the html file
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html = response.read().decode('utf-8', errors='ignore')
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parser = etree.XMLParser(recover=True)
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root = etree.fromstring(html, parser=parser)
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if root:
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for jsonld in root.findall(".//script[@type='application/ld+json']"):
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g.parse(data=jsonld.text, publicID=url, format='json-ld')
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fixedgraph = Graph()
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fixedgraph += [sanitize_triple(s) for s in g]
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print(g.serialize(format='turtle').decode('utf-8', errors='ignore'))
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@ -1,12 +1,22 @@
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'''
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Helper functions and ipython magic for the SPARQL exercises.
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The tests in the notebooks rely on the `LAST_QUERY` variable, which is updated by the `%%sparql` magic after every query.
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This variable contains the full query used (`LAST_QUERY["query"]`), the endpoint it was sent to (`LAST_QUERY["endpoint"]`), and a dictionary with the response of the endpoint (`LAST_QUERY["results"]`).
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For convenience, the results are also given as tuples (`LAST_QUERY["tuples"]`), and as a dictionary of of `{column:[values]}` (`LAST_QUERY["columns"]`).
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'''
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from IPython.core.magic import (register_line_magic, register_cell_magic,
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register_line_cell_magic)
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from IPython.display import HTML, display, Image
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from IPython.display import HTML, display, Image, display_javascript
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from urllib.request import Request, urlopen
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from urllib.parse import quote_plus, urlencode
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from urllib.error import HTTPError
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import json
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import sys
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js = "IPython.CodeCell.options_default.highlight_modes['magic_sparql'] = {'reg':[/^%%sparql/]};"
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display_javascript(js, raw=True)
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def send_query(query, endpoint):
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@ -20,7 +30,11 @@ def send_query(query, endpoint):
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headers={'content-type': 'application/x-www-form-urlencoded',
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'accept': FORMATS},
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method='POST')
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return json.loads(urlopen(r).read().decode('utf-8'));
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res = urlopen(r)
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data = res.read().decode('utf-8')
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if res.getcode() == 200:
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return json.loads(data)
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raise Exception('Error getting results: {}'.format(data))
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def tabulate(tuples, header=None):
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@ -39,11 +53,14 @@ def tabulate(tuples, header=None):
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LAST_QUERY = {}
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def solution():
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return LAST_QUERY
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def query(query, endpoint=None, print_table=False):
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global LAST_QUERY
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endpoint = endpoint or "http://dbpedia.org/sparql"
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endpoint = endpoint or "http://fuseki.cluster.gsi.dit.upm.es/sitc/"
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results = send_query(query, endpoint)
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tuples = to_table(results)
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@ -80,12 +97,30 @@ def to_table(results):
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@register_cell_magic
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def sparql(line, cell):
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'''
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Sparql magic command for ipython. It can be used in a cell like this:
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```
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%%sparql
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... Your SPARQL query ...
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```
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by default, it will use the DBpedia endpoint, but you can use a different endpoint like this:
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```
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%%sparql http://my-sparql-endpoint...
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... Your SPARQL query ...
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```
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'''
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try:
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return query(cell, endpoint=line, print_table=True)
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except HTTPError as ex:
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error_message = ex.read().decode('utf-8')
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print('Error {}. Reason: {}'.format(ex.status, ex.reason))
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print(error_message)
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print(error_message, file=sys.stderr)
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def show_photos(values):
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@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
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@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
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@prefix schema: <http://schema.org/> .
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_:Hotel1 a schema:Hotel ;
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schema:description "A fictitious hotel" .
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_:Review1 a schema:Review ;
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schema:reviewBody "This is a great review" ;
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schema:reviewRating [
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a schema:Rating ;
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schema:author <http://jfernando.es/me> ;
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schema:ratingValue "0.7"
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] ;
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schema:itemReviewed _:Hotel1 .
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_:Review2 a schema:Review ;
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schema:reviewBody "This is a not so great review" ;
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schema:reviewRating [
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a schema:Rating ;
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schema:author [ a schema:Person ;
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schema:givenName "anonymous" ] ;
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schema:ratingValue "0.3"
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] ;
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schema:itemReviewed _:Hotel1 .
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# !/bin/env python #
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# Ejemplo de consultas SPARQL sobre turtle #
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# python consultas.py #
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import rdflib
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import sys
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dataset = sys.argv[1] if len(sys.argv) > 1 else 'reviews.ttl'
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g = rdflib.Graph()
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schema = rdflib.Namespace("http://schema.org/")
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# Read Turtle file #
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g.parse(dataset, format='turtle')
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results = g.query(
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"""SELECT DISTINCT ?review ?p ?o
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WHERE {
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?review a schema:Review.
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?review ?p ?o.
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}""", initNs={'schema': schema})
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for row in results:
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print("%s %s %s" % row)
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import rdflib
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import sys
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g = rdflib.Graph()
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dataset = sys.argv[1] if len(sys.argv) > 1 else 'reviews.ttl'
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g.parse(dataset, format="n3")
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print(g.serialize(format="n3").decode('utf-8'))
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