def learn_new_triples(predicate):
"""learns new triples and stores them in a file"""
sc = get_sentence_classifier(predicate)
if predicate in numeric_predicates:
pattern = '<%s> <%s> "%s"^^<http://www.w3.org/2001/XMLSchema#int> .'
else:
pattern = '<%s> <%s> "%s"@pl .'
entities = CandidatesSelector.get_candidates(predicate)
entities = entities[: candidates_limit]
if verbose:
print '%s candidates identified' % len(entities)
n = 1000
entities_list = [
entities[i*n : (i+1)*n]
for i in xrange(int(ceil(len(entities) / n)))
]
out = open(results_path + 'triples-%s' % predicate, 'w')
ve = ValueExtractor(predicate, sc.extractor_training_data)
for entities in entities_list:
extracted_sentences = sc.extract_sentences(entities)
values = ve.extract_values(extracted_sentences)
for e, v in values.iteritems():
if v:
print >>out, pattern % (
full_resource_name(e).encode('utf-8'),
full_predicate_name(predicate).encode('utf-8'),
v
)
def learn_new_triples(predicate):
"""learns new triples and stores them in a file"""
sc = get_sentence_classifier(predicate)
if predicate in numeric_predicates:
pattern = '<%s> <%s> "%s"^^<http://www.w3.org/2001/XMLSchema#int> .'
else:
pattern = '<%s> <%s> "%s"@pl .'
entities = CandidatesSelector.get_candidates(predicate)
entities = entities[:candidates_limit]
if verbose:
print '%s candidates identified' % len(entities)
n = 1000
entities_list = [
entities[i * n:(i + 1) * n]
for i in xrange(int(ceil(len(entities) / n)))
]
out = open(results_path + 'triples-%s' % predicate, 'w')
ve = ValueExtractor(predicate, sc.extractor_training_data)
for entities in entities_list:
extracted_sentences = sc.extract_sentences(entities)
values = ve.extract_values(extracted_sentences)
for e, v in values.iteritems():
if v:
print >> out, pattern % (
full_resource_name(e).encode('utf-8'),
full_predicate_name(predicate).encode('utf-8'), v)
def __init__(self, predicate, training_data):
self.predicate = predicate
self.predominant_types = map(
lambda t: t.split('/')[-1],
CandidatesSelector.get_predominant_types(predicate, False)
)
self.model_filename = 'crfmodel-%s' % predicate
self.features_train_filename = 'features_train'
self.features_tag_filename = 'features_tag'
try:
open(models_cache_path % self.model_filename)
except IOError:
self.train(training_data)
