def get_sparql_store(query_endpoint=None,
update_endpoint=None,
sparql_user=None,
sparql_pass=None):
if not query_endpoint:
query_endpoint = current_app.config.get("SPARQL_ENDPOINT")
if not update_endpoint:
update_endpoint = current_app.config.get("SPARQL_UPDATE_ENDPOINT")
if not sparql_user:
sparql_user = current_app.config.get("SPARQL_USER")
if not sparql_pass:
sparql_pass = current_app.config.get("SPARQL_PASS")
auth = HTTPDigestAuth(sparql_user, sparql_pass)
store = SPARQLUpdateStore(queryEndpoint=query_endpoint,
update_endpoint=update_endpoint,
auth=auth,
context_aware=True,
postAsEncoded=False,
node_to_sparql=_node_to_sparql)
store.method = "POST"
store.formula_aware = True
return store
def __init__(self, endpoint):
self.endpoint = endpoint
query_ep = self.endpoint + '/query'
update_ep = self.endpoint + '/update'
self.sparql = SPARQLUpdateStore(queryEndpoint=query_ep,
update_endpoint=update_ep,
bNodeAsURI=True)
def create_sparql_store(endpoint, update_endpoint=None, use_let_syntax=False):
if update_endpoint == None:
update_endpoint = endpoint
store = SPARQLUpdateStore(queryEndpoint=endpoint,
update_endpoint=update_endpoint)
store.open((endpoint,update_endpoint))
return store
def update_fuseki(config, files):
"""
The current procedure first dumps the enriched graph to a temporary file in a dir accessible by
the web server, then loads the file using the SPARQL LOAD operation.
I first tried pushing the enriched graph directly to the update endpoint
without writing a temporary file, but that approach failed for two reasons:
- Using INSERT DATA with "lots" of triples (>> 20k) caused Fuseki to give a 500 response.
- Using INSERT DATA with chunks of 20k triples worked well... when there were no blank nodes.
If the same bnode were referenced in two different chunks, it would end up as *two* bnodes.
Since we're using bnodes in RDF lists, many lists ended up broken. From the SPARQL ref.:
Variables in QuadDatas are disallowed in INSERT DATA requests (see Notes 8 in the grammar).
That is, the INSERT DATA statement only allows to insert ground triples. Blank nodes in
QuadDatas are assumed to be disjoint from the blank nodes in the Graph Store,
i.e., will be inserted with "fresh" blank nodes.
Using tdbloader would be another option, but then we would still need a temp file, we would also need
to put that file on a volume accessible to the docker container, and we would need to shutdown the
server while loading the file. And it's a solution tied to Fuseki.
I'm not aware if there is a limit on how large graphs Fuseki can load with the LOAD operation.
I guess we'll find out.
"""
if config["dumps_dir"] is None:
raise Exception("The 'dumps_dir' option must be set")
if config["dumps_dir_url"] is None:
raise Exception("The 'dumps_dir_url' option must be set")
tmpfile = "{}/import_{}.ttl".format(config["dumps_dir"].rstrip("/"), config["basename"])
tmpfile_url = "{}/import_{}.ttl".format(config["dumps_dir_url"].rstrip("/"), config["basename"])
tc = enrich_and_concat(files, tmpfile)
c0 = get_graph_count(config)
store = SPARQLUpdateStore("{}/sparql".format(config["fuseki"]), "{}/update".format(config["fuseki"]))
graph_uri = URIRef(config["graph"])
graph = Graph(store, graph_uri)
logger.info("Fuseki: Loading %d triples into <%s> from %s", tc, graph_uri, tmpfile_url)
# CLEAR GRAPH first to make sure all blank nodes are erased
# https://github.com/scriptotek/emnesok/issues/70
store.update("CLEAR GRAPH <{}>".format(graph_uri))
store.update("LOAD <{}> INTO GRAPH <{}>".format(tmpfile_url, graph_uri))
c1 = get_graph_count(config)
if c0 == c1:
logger.info("Fuseki: Graph <%s> updated, number of concepts unchanged", config["graph"])
else:
logger.info("Fuseki: Graph <%s> updated, number of concepts changed from %d to %d.", config["graph"], c0, c1)
invalidate_varnish_cache(config["basename"])
logger.info("Invalidated Varnish cache for %s", config["basename"])
def _do_init(self, endpoint, username, password, **kwargs):
SPARQLUpdateStore.__init__(self, endpoint, endpoint,
node_to_sparql=_virtuoso_node_to_sparql,
node_from_result=_virtuoso_node_from_result,
**kwargs)
self.setHTTPAuth('digest')
self.setCredentials(username, password)
self.setReturnFormat = "json"
self.opened = True
def __init__(self, endpoint):
self.endpoint = endpoint
query_ep = self.endpoint + '/query'
update_ep = self.endpoint + '/update'
self.sparql = SPARQLUpdateStore(queryEndpoint=query_ep,
update_endpoint=update_ep,
bNodeAsURI=True)
def clear():
# Configurations
config = ConfigParser()
config.read('config.ini')
endpoint_uri = config['Mandatory']['endpointURI']
graph_uri = config['Mandatory']['graphURI']
clean_graph_query = "CLEAR GRAPH <" + graph_uri + ">"
# Set up endpoint and access to triple store
sparql = SPARQLWrapper(endpoint_uri)
sparql.setReturnFormat(JSON)
sparql.setMethod(POST)
store = SPARQLUpdateStore(endpoint_uri, endpoint_uri)
# Specify the (named) graph we're working with
sparql.addDefaultGraph(graph_uri)
# Create an in memory graph
g = Graph(store, identifier=graph_uri)
# Cleanup the existing triples
sparql.setQuery(clean_graph_query)
sparql.query().convert()
# Cleanup the graph instance
g.close()
def __init__(self, base_uri, endpoint=None):
self._base_uri = base_uri
if endpoint is None:
self._graph = Graph() # memory store
else:
default_graph = URIRef(base_uri)
store = SPARQLUpdateStore(endpoint)
self._graph = Graph(store, identifier=default_graph)
def endpoint(endpoint):
defaultgraph = None
store = SPARQLUpdateStore(queryEndpoint=endpoint,
update_endpoint=endpoint,
node_to_sparql=node_to_sparql,
node_from_result=node_from_result)
graph = ConjunctiveGraph(store, defaultgraph)
return graph
def __init__(self, config=None, create=None):
"""Create ontology database API with SQLAlchemy store.
Parameters
----------
config : [str, knowl.DBConfig], optional
The path to a configuration file or the configuration object. By default None,
which results in a configuration with default parameters (see knowl.DBConfig).
create : bool, optional
Whether or not the tables for the ontology (triplestore) should be initalized.
Set to True if you are creating a new database, by default None.
As per SQLAlchemy documentation, the creation operation is idempotent. Thus,
could be left at True, unless you specifically do not want to create a new database
if one does not exist.
"""
# initialize database config
self.__config = DBConfig.factory(config)
self.__username = None
self.__password = None
self.__create = create
self.__store_type = self.config["store"]
# configure database identifier (ontology IRI/base URL)
self.__identifier = self.config.baseURL
if self.store_type == "alchemy":
self.__store = SQLAlchemy(identifier=self.identifier)
self._graph = Graph(self.__store, identifier=self.identifier)
elif self.store_type == "fuseki":
self.__query_endpoint = f'http://{self.config["host"]}:{self.config["port"]}/{self.config["database"]}'
self.__update_endpoint = f'http://{self.config["host"]}:{self.config["port"]}/{self.config["database"]}/update'
self.__store = SPARQLUpdateStore(
queryEndpoint=self.__query_endpoint + '/sparql',
update_endpoint=self.__update_endpoint,
context_aware=True,
postAsEncoded=False,
node_to_sparql=my_bnode_ext)
self.__query_endpoint += '/query'
self.__store.method = 'POST'
else:
raise Exception(f"Unknown store type {self.store_type}!")
def engine_from_config(config, prefix):
defaultgraph = None
if prefix + "defaultGraph" in config:
defaultgraph = URIRef(config[prefix + "defaultGraph"])
if prefix + "queryEndpoint" in config:
store = SPARQLUpdateStore(
queryEndpoint=config[prefix + "queryEndpoint"],
update_endpoint=config[prefix + "updateEndpoint"],
default_query_method=POST,
returnFormat=JSON,
node_to_sparql=node_to_sparql)
store._defaultReturnFormat = JSON
store.setReturnFormat(JSON)
graph = ConjunctiveGraph(store, defaultgraph)
elif prefix + 'store' in config:
graph = ConjunctiveGraph(store='Sleepycat', identifier=defaultgraph)
graph.store.batch_unification = False
graph.store.open(config[prefix + "store"], create=True)
else:
graph = ConjunctiveGraph(identifier=defaultgraph)
return graph
def get_ds0():
update_endpoint = 'http://localhost:8890/sparql-auth'
# query_endpoint = 'http://localhost:8890/sparql'
store = SPARQLUpdateStore(update_endpoint,
update_endpoint,
autocommit=True)
store.setHTTPAuth(DIGEST)
store.setCredentials(user='******', passwd='admin')
return Dataset(store)
def __init__(self, database, measurement):
"""
initialize
:param database:
:param measurement:
"""
self.database = database
self.measurement = measurement
self.store = SPARQLUpdateStore(
queryEndpoint="{}:{}/{}/query".format(JF_HOST, JF_PORT,
JF_DATASET),
update_endpoint="{}:{}/{}/update".format(JF_HOST, JF_PORT,
JF_DATASET))
self.graph = Graph(identifier=URIRef(GRAPH_ID), store=self.store)
self.__init_graph()
def engine_from_config(config, prefix):
defaultgraph = None
if prefix+"defaultGraph" in config:
defaultgraph = URIRef(config[prefix+"defaultGraph"])
if prefix+"queryEndpoint" in config:
from rdflib.plugins.stores.sparqlstore import SPARQLUpdateStore
store = SPARQLUpdateStore(queryEndpoint=config[prefix+"queryEndpoint"],
update_endpoint=config[prefix+"updateEndpoint"])
graph = ConjunctiveGraph(store,defaultgraph)
elif prefix+'store' in config:
graph = ConjunctiveGraph(store='Sleepycat',identifier=defaultgraph)
graph.store.batch_unification = False
graph.store.open(config[prefix+"store"], create=True)
else:
graph = ConjunctiveGraph(identifier=defaultgraph)
return graph
def load(load_resource, resources):
print('Loading', load_resource.identifier)
file_graph = Dataset(default_union=True)
to_disk = False
for used in load_resource[prov.used]:
if used[RDF.type:setl.Persisted]:
to_disk = True
file_graph = Dataset(store='Sleepycat', default_union=True)
tempdir = tempfile.mkdtemp()
print("Gathering", load_resource.identifier, "into", tempdir)
file_graph.store.open(tempdir, True)
break
if len(list(load_resource[prov.used])) == 1:
file_graph = resources[load_resource.value(prov.used).identifier]
else:
for used in load_resource[prov.used]:
print("Using", used.identifier)
used_graph = resources[used.identifier]
file_graph.namespace_manager = used_graph.namespace_manager
#print used_graph.serialize(format="trig")
file_graph.addN(used_graph.quads())
for generated in load_resource.subjects(prov.wasGeneratedBy):
# TODO: support LDP-based loading
if generated[RDF.type:pv.File]:
fmt = generated.value(dc['format'])
if fmt is not None:
fmt = fmt.value
if fmt in formats:
fmt = formats[fmt]
print(fmt)
with open(generated.identifier.replace("file://", ''), 'wb') as o:
o.write(file_graph.serialize(format=fmt))
o.close()
elif generated[RDF.type:sd.Service]:
from rdflib.plugins.stores.sparqlstore import SPARQLUpdateStore
endpoint = generated.value(sd.endpoint,
default=generated).identifier
store = SPARQLUpdateStore(endpoint, endpoint, autocommit=False)
endpoint_graph = Dataset(store=store,
identifier=generated.identifier,
default_union=True)
endpoint_graph.addN(file_graph.quads())
endpoint_graph.commit()
if to_disk:
file_graph.close()
def __initStore(self):
store = SPARQLUpdateStore(queryEndpoint = getattr(settings,
'SPARQL_QUERY'),
update_endpoint = getattr(settings,
'SPARQL_UPDATE'),
postAsEncoded=False)
store.bind("rdf", "http://www.w3.org/1999/02/22-rdf-syntax-ns#")
store.bind("oa", "http://www.w3.org/ns/oa#")
store.bind("chnode", getattr(settings, 'NODE_URI', 'http://localhost'))
CharmeMiddleware.__store = store
#Creates a superuser if there is not any
try:
users = User.objects.all()
if len(users) == 0:
User.objects.create_superuser('admin', '', 'admin')
except DatabaseError:
LOGGING.error("Cannot find or create an application superuser")
def query(self, query, initNs, initBindings, queryGraph, **kwargs):
prepared_base = None
if hasattr(query, '_original_args'):
query, prepared_ns, prepared_base = query._original_args
if not initNs:
initNs = prepared_ns
else:
prepared_ns = dict(prepared_ns)
prepared_ns.update(initNs)
initNs = prepared_ns
base = kwargs.pop("base", None) or prepared_base
if base is not None:
query = '\n'.join([('BASE <%s>' % base), query])
res = SPARQLUpdateStore.query(self, query, initNs, initBindings, queryGraph, **kwargs)
if res.bindings is not None:
res.bindings = ( FrozenBindings(None, i) for i in res.bindings )
return res
def engine_from_config(config, prefix):
defaultgraph = None
if prefix + "defaultGraph" in config:
defaultgraph = URIRef(config[prefix + "defaultGraph"])
if prefix + "queryEndpoint" in config:
store = SPARQLUpdateStore(
queryEndpoint=config[prefix + "queryEndpoint"],
update_endpoint=config[prefix + "updateEndpoint"],
default_query_method=POST,
returnFormat=JSON,
node_to_sparql=node_to_sparql)
def publish(data, *graphs):
s = requests.session()
s.keep_alive = False
result = s.post(
store.endpoint,
data=data,
# params={"context-uri":graph.identifier},
headers={'Content-Type': 'application/x-trig'})
print store.endpoint, result.content
store.publish = publish
store._defaultReturnFormat = JSON
store.setReturnFormat(JSON)
graph = ConjunctiveGraph(store, defaultgraph)
elif prefix + 'store' in config:
graph = ConjunctiveGraph(store='Sleepycat', identifier=defaultgraph)
graph.store.batch_unification = False
graph.store.open(config[prefix + "store"], create=True)
else:
graph = ConjunctiveGraph(identifier=defaultgraph)
def publish(data, *graphs):
for nanopub in graphs:
graph.addN(nanopub.quads())
graph.store.publish = publish
return graph
def main():
# Track executing time
# start_time = time.time()
headers = {'content-type': 'application/json'} # HTTP header content type
# Configurations
config = ConfigParser()
config.read('config_3.ini')
endpoint_uri = config['Mandatory']['endpointURI']
graph_uri = config['Mandatory']['graphURI']
pool_uri = (config['Mandatory']['poolURI']).split(',')
type_uri = (config['Mandatory']['typeURI']).split(',')
# Set up endpoint and access to triple store
sparql = SPARQLWrapper(endpoint_uri)
sparql.setReturnFormat(JSON)
sparql.setMethod(POST)
store = SPARQLUpdateStore(endpoint_uri, endpoint_uri)
# Specify the (named) graph we're working with
sparql.addDefaultGraph(graph_uri)
# Create an in memory graph
g = Graph(store, identifier=graph_uri)
# Build the RDF from the JSON source data
# This function is to be called for each URL in the pool to harvest, in case that the source is in json, with the Estonian mapping
def rdf(urlrdf, f):
input = Graph()
input.open("store2", create=True)
input.parse(urlrdf, format=f)
for s, p, o in input:
g.add((s, p, o))
input.close()
def rdf_data(rdfobject, f):
input = ConjunctiveGraph()
input.open("store2", create=True)
input.parse(data=rdfobject, format=f)
#print(input.serialize(format='json-ld', auto_compact=True, indent=4))
for s, p, o in input:
g.add((s, p, o))
input.close()
# Set counter
c = 0
# Loop over all URI in the pool
while c < len(pool_uri):
#print(pool_uri[c],type_uri[c])
if type_uri[c] == 'xlsx':
url = "http://cpsv-ap.semic.eu/cpsv-ap_harvester/intapi/v1/importSpreadsheetFromURL?spreadsheetURL=" + urllib.parse.quote(
pool_uri[c])
text_json = requests.get(url).text
my_json = json.loads(text_json)
type_uri[c] = 'json-ld'
#validation
url = "https://www.itb.ec.europa.eu/shacl/cpsv-ap/api/validate"
if type_uri[c] == 'xml':
myobj = {
"contentSyntax": "application/rdf+xml",
"contentToValidate": pool_uri[c],
"embeddingMethod": "URL",
"reportSyntax": "application/ld+json"
}
if type_uri[c] == 'turtle':
myobj = {
"contentSyntax": "text/turtle",
"contentToValidate": pool_uri[c],
"embeddingMethod": "URL",
"reportSyntax": "application/ld+json"
}
if type_uri[c] == 'nt':
myobj = {
"contentSyntax": "application/n-triples",
"contentToValidate": pool_uri[c],
"embeddingMethod": "URL",
"reportSyntax": "application/ld+json"
}
if type_uri[c] == 'json-ld':
data = base64.urlsafe_b64encode(
text_json.encode("utf-8")).decode('utf-8')
myobj = {
"contentSyntax": "application/ld+json",
"contentToValidate": data,
"embeddingMethod": "BASE64",
"reportSyntax": "application/ld+json"
}
#myobj = { "contentSyntax": "application/ld+json", "contentToValidate": pool_uri[c], "embeddingMethod": "BASE64", "reportSyntax": "application/ld+json" }
#data = base64.urlsafe_b64encode(myobj).encode()).decode()
result_text_json = requests.post(url, json=myobj).text
my_json = json.loads(result_text_json)
#print(result_text_json)
#print(my_json.get("sh:conforms"))
if (my_json.get("sh:conforms") or type_uri[c] == 'jsonEstonia'):
#if 1:
#print(d2)
#print(d2['id'])
#print("with:colon is equal to {d['sh-conforms']}")
#print({'sh-conforms'}.format(**d))
#if (json_obj[0]["sh:conforms"] == true):
# print(pool_uri[c] + "is conform")
print("* " + pool_uri[c] +
" is conform to CPSV-AP and it is harvested")
if type_uri[c] == 'jsonEstonia':
try:
# Fetch the JSON data
response = requests.get(pool_uri[c],
headers=headers).json()
# Process the response
configJSON = ConfigParser()
configJSON.read('mapping_estonia.ini')
json_to_rdf(pool_uri[c], response, g, configJSON)
except ValueError as e:
print(e)
if type_uri[c] == 'xml' or type_uri[c] == 'turtle' or type_uri[
c] == 'nt':
rdf(pool_uri[c], type_uri[c])
if type_uri[c] == 'json-ld':
#print(text_json)
rdf_data(text_json, type_uri[c])
else:
print("* " + pool_uri[c] +
" is not conform to CPSV-AP and it is not harvested")
# Counter update
c += 1
# Iterate over triples in store and print them out.
print('\r\nNumber of triples added: %d' % len(g))
# Cleanup the graph instance
g.close()
headers = {'content-type': 'application/json'} # HTTP header content type
# Configurations
config = ConfigParser()
config.read('config_3.ini')
URI = sys.argv[1]
classType = sys.argv[2]
endpoint_uri = config['Mandatory']['endpointURI']
graph_uri = config['Mandatory']['graphURI']
# Set up endpoint and access to triple store
sparql = SPARQLWrapper(endpoint_uri)
sparql.setReturnFormat(JSON)
sparql.setMethod(POST)
store = SPARQLUpdateStore(endpoint_uri, endpoint_uri)
# Specify the (named) graph we're working with
sparql.addDefaultGraph(graph_uri)
# Create an in memory graph
g = Graph(store, identifier=graph_uri)
query = "select ?p ?o where {<"+ URI +"> ?p ?o}"
properties = g.query (query)
# Configurations mappings
mapping = ConfigParser()
mapping.read('mapping_fields.ini')
propURI = ""
def __init__(self, *args, **kwargs):
self.publish = None
SPARQLUpdateStore.__init__(self, *args, **kwargs)
mu.sameAs.append(rdflib.URIRef("http://d-nb.info/gnd/117726516"))
si.co_author.append(mu)
print(owlready.to_n3(scholars_ontology))
import rdflib
from rdflib.plugins.stores.sparqlstore import NSSPARQLWrapper,SPARQLUpdateStore,\
SPARQLUpdateStore
#store = SPARQLUpdateStore(queryEndpoint="http://localhost:8890/sparql",
# update_endpoint="http://localhost:8890/sparql-auth",
# )
store = SPARQLUpdateStore(queryEndpoint="http://localhost:10214/blazegraph/sparql",
update_endpoint="http://localhost:10214/blazegraph/sparql",
)
store.setNamespaceBindings({"ont":"http://ontologies.mpiwg-berlin.mpg.de/scholarlyRelations",
"end":"http://entity.mpiwg-berlin.mpg.de/modernPhysics"})
#g = rdflib.Graph(store=store,identifier="http://entities.mpiwg-berlin.mpg.de/graphs/gr/V14TEST")
#g.store.setCredentials('admin','admin')
#g.store.setHTTPAuth('DIGEST')
#owlready.to_rdflib(scholars_ontology,g)
class TruthyUpdater:
def __init__(self, endpoint, dryrun=False, user=None, passwd=None):
self.endpoint = SPARQLUpdateStore(endpoint,
default_query_method='POST')
if user:
self.endpoint.setCredentials(user, passwd)
self.dryrun = dryrun
def build_truthy(self, np_list):
self.insert_truthy_rank(np_list)
self.delete_normal_rank(np_list)
def update(self, query):
if self.dryrun:
action = 'INSERT' if 'INSERT' in query else 'DELETE'
query = query.replace(action, 'CONSTRUCT')
# print(query)
try:
res = self.endpoint.query(query)
print('### About to {} following triples:'.format(action))
for row in res:
print(' '.join(e.n3() for e in row))
except ResultException:
pass
else:
self.endpoint.update(query)
def insert_truthy_rank(self, np_list):
values = ' '.join(
'( wd:%(node)s p:%(p)s ps:%(p)s psn:%(p)s wdt:%(p)s wdtn:%(p)s )' %
{
'node': n,
'p': p
} for n, p in np_list)
query = '''
INSERT {
?statement a wikibase:BestRank .
?node ?wdt ?psv ;
?wdtn ?psnv .
} WHERE {
%s
{
?node ?p ?statement .
?statement wikibase:rank wikibase:PreferredRank ;
?ps ?psv .
OPTIONAL { ?statement ?psn ?psnv }
FILTER NOT EXISTS { ?statement a wikibase:BestRank }
}
UNION
{
?node ?p ?statement .
?statement wikibase:rank wikibase:NormalRank ;
?ps ?psv .
OPTIONAL { ?statement ?psn ?psnv }
FILTER NOT EXISTS { ?statement a wikibase:BestRank }
FILTER NOT EXISTS { ?node ?p [ wikibase:rank wikibase:PreferredRank ] }
}
}
''' % ('VALUES (?node ?p ?ps ?psn ?wdt ?wdtn ) { %s }' % values)
self.update(query)
def delete_normal_rank(self, np_list):
values = ' '.join('( wd:%(node)s p:%(p)s wdt:%(p)s wdtn:%(p)s )' % {
'node': n,
'p': p
} for n, p in np_list)
query = '''
DELETE {
?statement a wikibase:BestRank .
?node ?wdt ?value ;
?wdtn ?no .
} WHERE {
%s
?node ?p ?statement ;
?p [ wikibase:rank wikibase:PreferredRank ] .
?statement a wikibase:BestRank ;
wikibase:rank wikibase:NormalRank .
}
''' % ('VALUES (?node ?p ?wdt ?wdtn ) { %s }' % values)
self.update(query)
def _get_store():
store = SPARQLUpdateStore(queryEndpoint=SPARQL_QUERY,
update_endpoint=SPARQL_UPDATE,
postAsEncoded=False)
print("update:" + SPARQL_UPDATE)
return store
def __init__(self, endpoint, dryrun=False, user=None, passwd=None):
self.endpoint = SPARQLUpdateStore(endpoint,
default_query_method='POST')
if user:
self.endpoint.setCredentials(user, passwd)
self.dryrun = dryrun
def main():
# Track executing time
# start_time = time.time()
headers = {'content-type': 'application/json'} # HTTP header content type
# Configurations
config = ConfigParser()
config.read('config.ini')
endpoint_uri = config['Mandatory']['endpointURI']
graph_uri = config['Mandatory']['graphURI']
pool_uri = (config['Mandatory']['poolURI']).split(',')
type_uri = (config['Mandatory']['typeURI']).split(',')
# Set up endpoint and access to triple store
sparql = SPARQLWrapper(endpoint_uri)
sparql.setReturnFormat(JSON)
sparql.setMethod(POST)
store = SPARQLUpdateStore(endpoint_uri, endpoint_uri)
# Specify the (named) graph we're working with
sparql.addDefaultGraph(graph_uri)
# Create an in memory graph
g = Graph(store, identifier=graph_uri)
# Build the RDF from the JSON source data
# This function is to be called for each URL in the pool to harvest, in case that the source is in json, with the Estonian mapping
def rdf(urlrdf, f):
input = Graph()
input.open("store2", create=True)
input.parse(urlrdf, format=f)
for s, p, o in input:
g.add((s, p, o))
input.close()
# Set counter
c = 0
# Loop over all URI in the pool
while c < len(pool_uri):
print(pool_uri[c],type_uri[c])
if type_uri[c] == 'jsonEstonia':
try:
# Fetch the JSON data
response = requests.get(pool_uri[c], headers=headers).json()
# Process the response
configJSON = ConfigParser()
configJSON.read('mapping_estonia.ini')
json_to_rdf(pool_uri[c], response, g, configJSON)
except ValueError as e:
print(e)
if type_uri[c] == 'xml' or type_uri[c] == 'turtle' or type_uri[c] == 'nt':
rdf(pool_uri[c], type_uri[c])
# Counter update
c += 1
# Iterate over triples in store and print them out.
print('\r\nNumber of triples added: %d' % len(g))
# Cleanup the graph instance
g.close()
class OntologyDatabase:
"""A front-end of an ontology database. This class provides "safe" access to most of the standard
operations provided by the rdflib.Graph class. The "safeness" of the methods lies in catching
exceptions and reconnecting shall the connection to the database "die" for whatever reason.
Additionally, this class implements the SQLAlchemy store for the triples
"""
def __init__(self, config=None, create=None):
"""Create ontology database API with SQLAlchemy store.
Parameters
----------
config : [str, knowl.DBConfig], optional
The path to a configuration file or the configuration object. By default None,
which results in a configuration with default parameters (see knowl.DBConfig).
create : bool, optional
Whether or not the tables for the ontology (triplestore) should be initalized.
Set to True if you are creating a new database, by default None.
As per SQLAlchemy documentation, the creation operation is idempotent. Thus,
could be left at True, unless you specifically do not want to create a new database
if one does not exist.
"""
# initialize database config
self.__config = DBConfig.factory(config)
self.__username = None
self.__password = None
self.__create = create
self.__store_type = self.config["store"]
# configure database identifier (ontology IRI/base URL)
self.__identifier = self.config.baseURL
if self.store_type == "alchemy":
self.__store = SQLAlchemy(identifier=self.identifier)
self._graph = Graph(self.__store, identifier=self.identifier)
elif self.store_type == "fuseki":
self.__query_endpoint = f'http://{self.config["host"]}:{self.config["port"]}/{self.config["database"]}'
self.__update_endpoint = f'http://{self.config["host"]}:{self.config["port"]}/{self.config["database"]}/update'
self.__store = SPARQLUpdateStore(
queryEndpoint=self.__query_endpoint + '/sparql',
update_endpoint=self.__update_endpoint,
context_aware=True,
postAsEncoded=False,
node_to_sparql=my_bnode_ext)
self.__query_endpoint += '/query'
self.__store.method = 'POST'
else:
raise Exception(f"Unknown store type {self.store_type}!")
def setup(self, create=False, username: str = None, password: str = None):
"""Sets-up a new database connection. Call this to initialize access to the database.
Parameters
----------
create : bool, optional
Whether the tables should be created (idempotent). Only set to True if creating a new database, by default False.
Setting the object property self.create to anything but None will override this value!
username : str, optional
Database access credentials. Only set this if you didn't set it before (e.g. in the config file), by default None
password : str, optional
Database access credentials. Only set this if you didn't set it before (e.g. in the config file), by default None
"""
if self.store_type == "alchemy":
if self.__create is not None:
create = self.__create
self._graph.open(self.config.getDB_URI(
self.__username if username is None else username,
self.__password if password is None else password),
create=create)
elif self.store_type == "fuseki":
print(
f"Query endpoint: {self.__query_endpoint}\nUpdate endpoint: {self.__update_endpoint}\nIndentifier: {self.identifier}"
)
self.__store.open((self.__query_endpoint, self.__update_endpoint))
self._graph = Graph(self.__store, identifier=self.identifier)
for ns, uri in self.config.namespaces.items():
self._graph.bind(ns.lower(), uri)
def closelink(self):
"""Closes the database connection.
"""
try:
self._graph.close()
except Exception as e:
print("Exception in Closing", e)
def destroy(self, confirmation: str = None):
"""Destroys the store for the Ontology
This will erase/destroy the database (triplestore) used to store the data.
Be very careful when calling this function.
Parameters
----------
confirmation : str, optional
[description], by default None
"""
if confirmation == "I know what I am doing":
self._graph.destroy(self.identifier)
else:
raise ValueError(
"Destroying the DB attempted but failed - wrong confirmation string!"
)
def setCredentials(self, username: str = None, password: str = None):
"""Set access credentials for the database server containing the triplestore.
Parameters
----------
username : str, optional
The username, by default None
password : str, optional
The password. Warning, this will be visible in the DB URL! By default None
"""
self.__username = username
self.__password = password
@interact_with_db
def mergeFileIntoDB(self, filepath: str):
"""Merge an existing ontology file into the current database. This could be used to populate
a new ontology from an existing one stored as a file. The ontology is automatically merged
and stored in the triplestore database server after calling this function.
Parameters
----------
filepath : str
Path to the file containing the ontology. See RDFLib documentation,
specifically, the function Graph.parse for supported formats.
"""
tmpGraph = Graph()
tmpGraph.parse(filepath)
self._graph += tmpGraph
@property
def config(self):
return self.__config
@property
def identifier(self):
return self.__identifier
@property
def store_type(self):
return self.__store_type
@interact_with_db
def bind(self, prefix, namespace, override=True):
self._graph.bind(prefix.lower(), namespace, override)
@interact_with_db
def query(self, *args, **kwargs) -> Generator:
return self._graph.query(*args, **kwargs)
@interact_with_db
def update(self, *args, **kwargs) -> Generator:
return self._graph.update(*args, **kwargs)
@interact_with_db
def add(self, triple: tuple):
"""Adds a triple (s, p, o) into the database.
This function is only a "safe" re-implementation of the original rdflib graph function.
See rdflib.Graph documentation for more information.
Parameters
----------
triple : tuple
(s, p, o) triple
"""
self._graph.add(triple)
@interact_with_db
def addN(self, triples: list):
"""Adds n-triples into the database. This method is faster than adding
individual triples one-by-one using the "add" method. This function
also automatically adds the current graph as the context (unlike
the original method from rdflib).
This function is only a "safe" re-implementation of the original rdflib graph function.
See rdflib.Graph documentation for more information.
Parameters
----------
triples : list
list of (s, p, o) triples to be added into the database
"""
# automatically add self.graph as context if not specified directly
quads = [t + (self._graph, ) for t in triples if len(t) == 3]
self._graph.addN(quads)
@interact_with_db
def remove(self, triple: tuple):
"""Remove the specified triple or triples from the database.
Not all fields of the triple needs to be specified. Omitted parts
shall be replaced with "None" value.
In such case, all triples matching the provided variables
are removed.
E.g., (someEntity, None, None) will remove containing "someEntity"
as subject.
This function is only a "safe" re-implementation of the original rdflib graph function.
See rdflib.Graph documentation for more information.
Parameters
----------
triple : tuple
(s, p, o) triple
"""
self._graph.remove(triple)
@interact_with_db
def triples(self, triple: tuple):
"""Returns a generator for triples matching the provided pattern.
The pattern/template triple can contain concrete values or None
where the item shall be matched to anything.
E.g., (None, RDF.type, None) will return all triples containing
RDF.type as the predicate.
This function is only a "safe" re-implementation of the original rdflib graph function.
See rdflib.Graph documentation for more information.
Parameters
----------
triple : tuple
(s, p, o) triple
Returns
-------
generator
generator of matching triples
"""
return self._graph.triples(triple)
@interact_with_db
def subjects(self,
predicate: Identifier = None,
object: Identifier = None):
"""Returns a (list of) subject(s) matching the values provided as predicate
and object. Similarly to triples, wildcard items can be replaced with None.
This function is only a "safe" re-implementation of the original rdflib graph function.
See rdflib.Graph documentation for more information.
Parameters
----------
predicate : Identifier, optional
p, by default None
object : Identifier, optional
o, by default None
Returns
-------
generator
Subjects matching the query
"""
return self._graph.subjects(predicate, object)
@interact_with_db
def subject_objects(self, predicate: Identifier = None):
"""Returns subjects and objects matching the value provided as predicate.
See "subjects" and "triples" methods for more info.
This function is only a "safe" re-implementation of the original rdflib graph function.
See rdflib.Graph documentation for more information.
Parameters
----------
predicate : Identifier, optional
p, by default None
Returns
-------
generator
The subjects and objects matching where predicate is set to the provided value
"""
return self._graph.subject_objects(predicate)
@interact_with_db
def subject_predicates(self, object: Identifier = None):
"""Returns subjects and predicates matching the value provided as object.
See "subjects" and "triples" methods for more info.
This function is only a "safe" re-implementation of the original rdflib graph function.
See rdflib.Graph documentation for more information.
Parameters
----------
object : Identifier, optional
o, by default None
Returns
-------
generator
The subjects and predicates matching the query
"""
return self._graph.subject_predicates(object)
@interact_with_db
def objects(self,
subject: Identifier = None,
predicate: Identifier = None):
"""Returns (a list of) object(s) matching the query.
See "subjects" and "triples" methods for more info.
This function is only a "safe" re-implementation of the original rdflib graph function.
See rdflib.Graph documentation for more information.
Parameters
----------
subject : Identifier, optional
s, by default None
predicate : Identifier, optional
p, by default None
Returns
-------
generator
The objects matching the query
"""
return self._graph.objects(subject, predicate)
@interact_with_db
def predicates(self,
subject: Identifier = None,
object: Identifier = None):
"""Returns (a list of) predicate(s) matching the query.
See "subjects" and "triples" methods for more info.
This function is only a "safe" re-implementation of the original rdflib graph function.
See rdflib.Graph documentation for more information.
Parameters
----------
subject : Identifier, optional
s, by default None
object : Identifier, optional
o, by default None
Returns
-------
generator
The predicates matching the query.
"""
return self._graph.predicates(subject, object)
@interact_with_db
def predicate_objects(self, subject: Identifier = None):
"""Returns predicates and objects where the subject matches
the value specified in the function parameter.
See "subjects" and "triples" methods for more info.
This function is only a "safe" re-implementation of the original rdflib graph function.
See rdflib.Graph documentation for more information.
Parameters
----------
subject : Identifier, optional
s, by default None
Returns
-------
generator
The predicates and objects matching the query
"""
return self._graph.predicate_objects(subject)
@interact_with_db
def transitive_subjects(self, predicate: Identifier, object: Identifier):
"""This function transitively generates subjects for the object,
using only the value specified as predicate as the property.
I.e., it "walks backwards" using only the predicate.
E.g., transitive_subjects(parentOf, entity) will generate
all ancestor of the object "entity".
See "subjects" and "triples" methods for more info.
This function is only a "safe" re-implementation of the original rdflib graph function.
See rdflib.Graph documentation for more information.
Parameters
----------
predicate : Identifier
p
object : Identifier
o
Returns
-------
generator
Generator of subjects matching the query.
"""
return self._graph.transitive_subjects(predicate, object)
@interact_with_db
def transitive_objects(self, subject: Identifier, property: Identifier):
"""This function generates objects for the subject using only the property.
It is the revers of "transitive_subjects". I.e., it "walks forwards"
in the ontology, using only the property/predicate.
E.g., transitive_objects(entity, hasComponent) will generate all objects
that are part of the entity (i.e., all the components of the entity).
See "subjects" and "triples" methods for more info.
This function is only a "safe" re-implementation of the original rdflib graph function.
See rdflib.Graph documentation for more information.
Parameters
----------
subject : Identifier
s
property : Identifier
p
Returns
-------
generator
Objects matchting the query
"""
return self._graph.transitive_objects(subject, property)
@interact_with_db
def set(self, triple: set):
"""Convenience function for "set" operations in the database.
Values set by this function are first removed and than assigned,
ensuring there is only one record for the specified subject + property.
This function is only a "safe" re-implementation of the original rdflib graph function.
See rdflib.Graph documentation for more information.
Parameters
----------
triple : set
(s, p, o) triple
"""
self._graph.set(triple)
@interact_with_db
def value(self,
subject: Identifier = None,
predicate: Identifier = RDF.value,
object: Identifier = None,
default=None,
any=True):
"""Complementery function for the "set" method. It expects that there is only one value
matching the subject + predicate combination. Error is risen otherwise!
This function is only a "safe" re-implementation of the original rdflib graph function.
See rdflib.Graph documentation for more information.
Parameters
----------
subject : Identifier, optional
s, by default None
predicate : Identifier, optional
p, by default RDF.value
object : Identifier, optional
o, by default None
default : any, optional
Default value to be returned if it is not specified in the database, by default None
any : bool, optional
No idea, see rdflib documentation, by default True
Returns
-------
any
The expected value
"""
return self._graph.value(subject, predicate, object, default, any)
@interact_with_db
def compute_qname(self, uri):
return self._graph.compute_qname(uri)
@interact_with_db
def __getitem__(self, item):
"""Convenience function. Allows queries/triples to be specified via the "object[index]"
notation.
This function is only a "safe" re-implementation of the original rdflib graph function.
See rdflib.Graph documentation for more information.
"""
return self._graph.__getitem__(item)
@interact_with_db
def __len__(self):
"""Allows the use of the len(container) function to return the number of entries in the database
This function is only a "safe" re-implementation of the original rdflib graph function.
See rdflib.Graph documentation for more information.
"""
return len(self._graph)
@interact_with_db
def __contains__(self, item):
"""Allows the use of "item in container" notation to be used to test if database contains entries
matching the query. The item shall be an (s, p, o) triple, obeying the standard contrains.
This function is only a "safe" re-implementation of the original rdflib graph function.
See rdflib.Graph documentation for more information.
"""
return item in self._graph
@property
def graph(self):
# REMOVE: temporal debugging property, shall not be present at release
return self._graph
class Store:
def __init__(self, endpoint):
self.endpoint = endpoint
query_ep = self.endpoint + '/query'
update_ep = self.endpoint + '/update'
self.sparql = SPARQLUpdateStore(queryEndpoint=query_ep,
update_endpoint=update_ep,
bNodeAsURI=True)
def store_annotations(self, annotations):
for annotation in annotations:
ann = Annotation()
ann.parse_json(annotation)
ann.add_to_graph(self.sparql)
return
def query_article(self, article):
escaped = self.escape_sparql(article)
ret = []
query = """
SELECT DISTINCT ?author ?author_fullname ?author_email
?date ?label ?type ?body_s ?body_p ?body_o ?body_l
?target_start ?target_startoffset ?target_endoffset
WHERE {
?annotation rdf:type oa:Annotation ;
oa:annotatedAt ?date ;
oa:annotatedBy ?author .
OPTIONAL { ?author foaf:name ?author_fullname }
OPTIONAL { ?author schema:email ?author_email }
OPTIONAL { ?annotation rdfs:label ?label }
OPTIONAL { ?annotation ao:type ?type }
OPTIONAL { ?annotation oa:hasBody ?body }
OPTIONAL { ?body rdf:subject ?body_s }
OPTIONAL { ?body rdf:predicate ?body_p }
OPTIONAL { ?body rdf:object ?body_o }
OPTIONAL { ?body rdfs:label ?body_l }
{ ?annotation oa:hasTarget ao:""" + escaped + """ }
UNION
{ ?annotation oa:hasTarget ?bnode .
?bnode rdf:type oa:SpecificResource ;
oa:hasSource ao:""" + escaped + """ ;
oa:hasSelector ?selector .
?selector rdf:type oa:FragmentSelector ;
rdf:value ?target_start ;
oa:start ?target_startoffset ;
oa:end ?target_endoffset }
}
"""
for row in self.sparql.query(query, initNs=initNS):
annotation = Annotation()
annotation.parse_rdf({
'target': article,
'author': row[0].encode('utf-8'),
'author_fullname': row[1].encode('utf-8') if row[1] is not None else None,
'author_email': row[2].encode('utf-8') if row[2] is not None else None,
'created': row[3].encode('utf-8') if row[3] is not None else None,
'label': row[4].encode('utf-8') if row[4] is not None else None,
'type': row[5].encode('utf-8') if row[5] is not None else None,
'subject': row[6].encode('utf-8') if row[6] is not None else None,
'predicate': row[7].encode('utf-8') if row[7] is not None else None,
'object': row[8].encode('utf-8') if row[8] is not None else None,
'obj_label': row[9].encode('utf-8') if row[9] is not None else None,
'target_start': row[10].encode('utf-8') if row[10] is not None else None,
'target_startoff': int(row[11]) if row[11] is not None else None,
'target_endoff': int(row[12]) if row[12] is not None else None
})
ret.append(annotation.to_dict())
return ret
def query_authors(self):
authors = []
query = """
SELECT DISTINCT ?author ?author_fullname ?author_email
WHERE {
?author a foaf:Person ;
foaf:name ?author_fullname .
OPTIONAL { ?author schema:email ?author_email }
}
"""
for row in self.sparql.query(query, initNs=initNS):
authors.append({
'author_id': row[0].encode('utf-8'),
'author_fullname': row[1].encode('utf-8'),
'author_email': row[2].encode('utf-8') if row[2] is not None else None,
})
return authors
# Inserts a new author.
# Expects a dict:
# {
# 'author_id': ...,
# 'author_fullname': ...,
# 'author_email': ...
# }
def insert_author(self, author):
try:
parse(author['author_id'], rule='IRI')
a = author['author_id']
except ValueError:
a = AOP[author['author_id']]
self.sparql.add((a, RDF.type, FOAF.Person))
self.sparql.add((a, FOAF.name, Literal(author['author_fullname'])))
if 'author_email' in author:
self.sparql.add((a, SCHEMA.email, Literal(author['author_email'])))
return 'OK'
def query_organization(self):
ret = []
query = """
SELECT DISTINCT ?node ?label
WHERE {
?node a foaf:Organization ;
foaf:name ?label .
}
"""
for row in self.sparql.query(query, initNs=initNS):
ret.append({
'id': row[0].encode('utf-8'),
'label': row[1].encode('utf-8')
})
return ret
def query_place(self):
ret = []
query = """
SELECT DISTINCT ?node ?label
WHERE {
?node a dbpedia:Place ;
rdfs:label ?label .
}
"""
for row in self.sparql.query(query, initNs=initNS):
ret.append({
'id': row[0].encode('utf-8'),
'label': row[1].encode('utf-8')
})
return ret
def query_concept(self):
ret = []
query = """
SELECT DISTINCT ?node ?label
WHERE {
?node a skos:Concept ;
rdfs:label ?label .
}
"""
for row in self.sparql.query(query, initNs=initNS):
ret.append({
'id': row[0].encode('utf-8'),
'label': row[1].encode('utf-8')
})
return ret
def insert_organization(self, data):
try:
parse(data['id'], rule='IRI')
a = URIRef(data['id'])
except ValueError:
a = AOP[data['id']]
self.sparql.add((a, RDF.type, FOAF.Organization))
self.sparql.add((a, FOAF.name, Literal(data['label'])))
return 'OK'
def insert_place(self, data):
try:
parse(data['id'], rule='IRI')
a = URIRef(data['id'])
except ValueError:
a = DBPEDIA[data['id']]
self.sparql.add((a, RDF.type, DBPEDIA.Place))
self.sparql.add((a, RDFS.label, Literal(data['label'])))
return 'OK'
def insert_concept(self, data):
try:
parse(data['id'], rule='IRI')
a = URIRef(data['id'])
except ValueError:
a = BNCF[data['id']]
self.sparql.add((a, RDF.type, SKOS.Concept))
self.sparql.add((a, RDFS.label, Literal(data['label'])))
return 'OK'
@staticmethod
def init_graph():
rdf = Graph()
for ns in initNS:
rdf.bind(ns, initNS[ns])
return rdf
@staticmethod
def escape_sparql(string):
return string.replace('(', '\(').replace(')', '\)')
def update_fuseki(config, files):
"""
The current procedure first dumps the enriched graph to a temporary file in a dir accessible by
the web server, then loads the file using the SPARQL LOAD operation.
I first tried pushing the enriched graph directly to the update endpoint
without writing a temporary file, but that approach failed for two reasons:
- Using INSERT DATA with "lots" of triples (>> 20k) caused Fuseki to give a 500 response.
- Using INSERT DATA with chunks of 20k triples worked well... when there were no blank nodes.
If the same bnode were referenced in two different chunks, it would end up as *two* bnodes.
Since we're using bnodes in RDF lists, many lists ended up broken. From the SPARQL ref.:
Variables in QuadDatas are disallowed in INSERT DATA requests (see Notes 8 in the grammar).
That is, the INSERT DATA statement only allows to insert ground triples. Blank nodes in
QuadDatas are assumed to be disjoint from the blank nodes in the Graph Store,
i.e., will be inserted with "fresh" blank nodes.
Using tdbloader would be another option, but then we would still need a temp file, we would also need
to put that file on a volume accessible to the docker container, and we would need to shutdown the
server while loading the file. And it's a solution tied to Fuseki.
I'm not aware if there is a limit on how large graphs Fuseki can load with the LOAD operation.
I guess we'll find out.
"""
if config['dumps_dir'] is None:
raise Exception("The 'dumps_dir' option must be set")
if config['dumps_dir_url'] is None:
raise Exception("The 'dumps_dir_url' option must be set")
tmpfile = '{}/import_{}.ttl'.format(config['dumps_dir'].rstrip('/'),
config['basename'])
tmpfile_url = '{}/import_{}.ttl'.format(
config['dumps_dir_url'].rstrip('/'), config['basename'])
tc = enrich_and_concat(files, tmpfile)
c0 = get_graph_count(config)
store = SPARQLUpdateStore('{}/sparql'.format(config['fuseki']),
'{}/update'.format(config['fuseki']))
graph_uri = URIRef(config['graph'])
graph = Graph(store, graph_uri)
logger.info("Fuseki: Loading %d triples into <%s> from %s", tc, graph_uri,
tmpfile_url)
# CLEAR GRAPH first to make sure all blank nodes are erased
# https://github.com/scriptotek/emnesok/issues/70
store.update('CLEAR GRAPH <{}>'.format(graph_uri))
store.update('LOAD <{}> INTO GRAPH <{}>'.format(tmpfile_url, graph_uri))
c1 = get_graph_count(config)
if c0 == c1:
logger.info('Fuseki: Graph <%s> updated, number of concepts unchanged',
config['graph'])
else:
logger.info(
'Fuseki: Graph <%s> updated, number of concepts changed from %d to %d.',
config['graph'], c0, c1)
invalidate_varnish_cache(config['basename'])
logger.info('Invalidated Varnish cache for %s', config['basename'])
def __addN(self, quads):
for batch in ibatch(quads, 100):
SPARQLUpdateStore.addN(self, quads)
class Store:
def __init__(self, endpoint):
self.endpoint = endpoint
query_ep = self.endpoint + '/query'
update_ep = self.endpoint + '/update'
self.sparql = SPARQLUpdateStore(queryEndpoint=query_ep,
update_endpoint=update_ep,
bNodeAsURI=True)
def store_annotations(self, annotations):
for annotation in annotations:
ann = Annotation()
ann.parse_json(annotation)
ann.add_to_graph(self.sparql)
return
def query_article(self, article):
escaped = self.escape_sparql(article)
ret = []
query = """
SELECT DISTINCT ?author ?author_fullname ?author_email
?date ?label ?type ?body_s ?body_p ?body_o ?body_l
?target_start ?target_startoffset ?target_endoffset
WHERE {
?annotation rdf:type oa:Annotation ;
oa:annotatedAt ?date ;
oa:annotatedBy ?author .
OPTIONAL { ?author foaf:name ?author_fullname }
OPTIONAL { ?author schema:email ?author_email }
OPTIONAL { ?annotation rdfs:label ?label }
OPTIONAL { ?annotation ao:type ?type }
OPTIONAL { ?annotation oa:hasBody ?body }
OPTIONAL { ?body rdf:subject ?body_s }
OPTIONAL { ?body rdf:predicate ?body_p }
OPTIONAL { ?body rdf:object ?body_o }
OPTIONAL { ?body rdfs:label ?body_l }
{ ?annotation oa:hasTarget ao:""" + escaped + """ }
UNION
{ ?annotation oa:hasTarget ?bnode .
?bnode rdf:type oa:SpecificResource ;
oa:hasSource ao:""" + escaped + """ ;
oa:hasSelector ?selector .
?selector rdf:type oa:FragmentSelector ;
rdf:value ?target_start ;
oa:start ?target_startoffset ;
oa:end ?target_endoffset }
}
"""
for row in self.sparql.query(query, initNs=initNS):
annotation = Annotation()
annotation.parse_rdf({
'target':
article,
'author':
row[0].encode('utf-8'),
'author_fullname':
row[1].encode('utf-8') if row[1] is not None else None,
'author_email':
row[2].encode('utf-8') if row[2] is not None else None,
'created':
row[3].encode('utf-8') if row[3] is not None else None,
'label':
row[4].encode('utf-8') if row[4] is not None else None,
'type':
row[5].encode('utf-8') if row[5] is not None else None,
'subject':
row[6].encode('utf-8') if row[6] is not None else None,
'predicate':
row[7].encode('utf-8') if row[7] is not None else None,
'object':
row[8].encode('utf-8') if row[8] is not None else None,
'obj_label':
row[9].encode('utf-8') if row[9] is not None else None,
'target_start':
row[10].encode('utf-8') if row[10] is not None else None,
'target_startoff':
int(row[11]) if row[11] is not None else None,
'target_endoff':
int(row[12]) if row[12] is not None else None
})
ret.append(annotation.to_dict())
return ret
def query_authors(self):
authors = []
query = """
SELECT DISTINCT ?author ?author_fullname ?author_email
WHERE {
?author a foaf:Person ;
foaf:name ?author_fullname .
OPTIONAL { ?author schema:email ?author_email }
}
"""
for row in self.sparql.query(query, initNs=initNS):
authors.append({
'author_id':
row[0].encode('utf-8'),
'author_fullname':
row[1].encode('utf-8'),
'author_email':
row[2].encode('utf-8') if row[2] is not None else None,
})
return authors
# Inserts a new author.
# Expects a dict:
# {
# 'author_id': ...,
# 'author_fullname': ...,
# 'author_email': ...
# }
def insert_author(self, author):
try:
parse(author['author_id'], rule='IRI')
a = author['author_id']
except ValueError:
a = AOP[author['author_id']]
self.sparql.add((a, RDF.type, FOAF.Person))
self.sparql.add((a, FOAF.name, Literal(author['author_fullname'])))
if 'author_email' in author:
self.sparql.add((a, SCHEMA.email, Literal(author['author_email'])))
return 'OK'
def query_organization(self):
ret = []
query = """
SELECT DISTINCT ?node ?label
WHERE {
?node a foaf:Organization ;
foaf:name ?label .
}
"""
for row in self.sparql.query(query, initNs=initNS):
ret.append({
'id': row[0].encode('utf-8'),
'label': row[1].encode('utf-8')
})
return ret
def query_place(self):
ret = []
query = """
SELECT DISTINCT ?node ?label
WHERE {
?node a dbpedia:Place ;
rdfs:label ?label .
}
"""
for row in self.sparql.query(query, initNs=initNS):
ret.append({
'id': row[0].encode('utf-8'),
'label': row[1].encode('utf-8')
})
return ret
def query_concept(self):
ret = []
query = """
SELECT DISTINCT ?node ?label
WHERE {
?node a skos:Concept ;
rdfs:label ?label .
}
"""
for row in self.sparql.query(query, initNs=initNS):
ret.append({
'id': row[0].encode('utf-8'),
'label': row[1].encode('utf-8')
})
return ret
def insert_organization(self, data):
try:
parse(data['id'], rule='IRI')
a = URIRef(data['id'])
except ValueError:
a = AOP[data['id']]
self.sparql.add((a, RDF.type, FOAF.Organization))
self.sparql.add((a, FOAF.name, Literal(data['label'])))
return 'OK'
def insert_place(self, data):
try:
parse(data['id'], rule='IRI')
a = URIRef(data['id'])
except ValueError:
a = DBPEDIA[data['id']]
self.sparql.add((a, RDF.type, DBPEDIA.Place))
self.sparql.add((a, RDFS.label, Literal(data['label'])))
return 'OK'
def insert_concept(self, data):
try:
parse(data['id'], rule='IRI')
a = URIRef(data['id'])
except ValueError:
a = BNCF[data['id']]
self.sparql.add((a, RDF.type, SKOS.Concept))
self.sparql.add((a, RDFS.label, Literal(data['label'])))
return 'OK'
@staticmethod
def init_graph():
rdf = Graph()
for ns in initNS:
rdf.bind(ns, initNS[ns])
return rdf
@staticmethod
def escape_sparql(string):
return string.replace('(', '\(').replace(')', '\)')
data_class = "".join(e for e in (numberOfLines + "_" + subject)
if e.isalnum())
timeAndDate = getTimeAndDate(text)
day = timeAndDate[0]
date = timeAndDate[1]
date_class = date.replace(" ", "_")
time = timeAndDate[2]
timezone = timeAndDate[3]
time_class = time.replace(":", "_")
date_and_time_class = date_class + "_" + time_class
# FUSEKI
from rdflib.plugins.stores.sparqlstore import SPARQLUpdateStore
from rdflib.graph import ConjunctiveGraph
updateStore = SPARQLUpdateStore("http://localhost:3030/RM/update")
# updateStore = SPARQLUpdateStore("http://localhost:3030/store/update")
updateGraph = ConjunctiveGraph(store=updateStore)
insertPerson(updateGraph, person_class, first_name, last_name, email)
insertOrganization(updateGraph, organization_class, organization_name,
distribution)
insertData(updateGraph, data_class, subject, summary, numberOfLines)
insertNewsgroup(updateGraph, newsgroup_class, newsgroup_name)
insertDate(updateGraph, date_class, date, day)
insertTime(updateGraph, time_class, time, timezone)
createNews(updateGraph, news_class, newsgroup_class, person_class,
organization_class, author_class, data_class, date_class,
time_class, date_and_time_class)
import os
from urllib.error import HTTPError
from SPARQLWrapper import DIGEST, POST
from SPARQLWrapper.SPARQLExceptions import EndPointNotFound
from rdflib.graph import Dataset, ReadOnlyGraphAggregate
from rdflib.namespace import RDF, RDFS, OWL, Namespace, URIRef
from rdflib.plugins.stores.sparqlstore import SPARQLUpdateStore
from rdflib.plugins.stores.sparqlstore import SPARQLWrapper
from experiments.ontology.config import config
from experiments.utils import except_safe
ont_config = config['ontology']
endpoint = update_endpoint = ont_config['endpoint']
store = SPARQLUpdateStore(endpoint, update_endpoint, autocommit=True) # need to call store.commit explicitly todo: there's some trouble in sparqlstore's source with that autocommit logic
store.setHTTPAuth(DIGEST)
store.setCredentials(user=ont_config['endpoint_user'], passwd=ont_config['endpoint_passwd'])
ds = Dataset(store, default_union=False)
iri_dbo = 'http://dbpedia.org/ontology'
iri_dbpedia = 'http://dbpedia.org'
iri_labels = 'http://dbpedia.org/labels'
iri_redirects = 'http://dbpedia.org/redirects'
iri_disamb = 'http://dbpedia.org/disambiguations'
iri_field = 'field'
iri_more = 'field:more'
dbo = Namespace('http://dbpedia.org/ontology/')
dbr = Namespace('http://dbpedia.org/resource/')
# NB: for existing graphs use 'ds.get_context(iri)', for new graphs use 'ds.graph(iri)'