def get_semantic_relations(self, entity_pairs, is_id=False):
if is_id:
rels = tagme.relatedness_wid(entity_pairs)
else:
rels = tagme.relatedness_title(entity_pairs)
return [{'entity1': rel.title1, 'entity2': rel.title2, 'score': rel.rel} \
for rel in rels.relatedness]
def similarity(A, B, flag=0):
if flag == 0:
rels = tagme.relatedness_title((A, B))
return rels.relatedness[0].rel
else:
rels = tagme.relatedness_wid((A, B))
return rels.relatedness[0].rel
def main():
# Annotate a text.
print("Annotating text: ", SAMPLE_TEXT)
#resp = tagme.annotate(SAMPLE_TEXT)
resp = tagme.annotate(SAMPLE_TEXT, include_categories=True)
print(resp)
for ann in resp.annotations:
print(ann)
# Find mentions in a text.
print("Finding mentions in text: ", SAMPLE_TEXT)
resp = tagme.mentions(SAMPLE_TEXT)
print(resp)
for mention in resp.mentions:
print(mention)
# Find relatedness between one pair of entities, by title.
resp = tagme.relatedness_title(["Barack_Obama", "Italy"])
print(resp)
for rel in resp.relatedness:
print(rel)
# Find relatedness between pairs of entities, by title.
resp = tagme.relatedness_title([("Barack_Obama", "Italy"),
("Italy", "Germany"),
("Italy", "BAD ENTITY NAME")])
print(resp)
for rel in resp.relatedness:
print(rel)
# Access the relatedness response as a dictionary.
resp_dict = dict(resp)
print("Relatedness between Italy and Germany: ",
resp_dict[("Italy", "Germany")])
# Find relatedness between one pair of entities, by wikipedia id
resp = tagme.relatedness_wid((31717, 534366))
print(resp)
for rel in resp.relatedness:
print(rel)
# Find relatedness between pairs of entities, by wikipedia id
resp = tagme.relatedness_wid([(534366, 534366 + a) for a in range(1010)])
print(resp)
for rel in resp.relatedness:
print(rel)
def _prefetch_relatedness(self, entity_group_1, entity_group_2):
pairs = ((e1, e2) for e1 in entity_group_1 for e2 in entity_group_2)
pairs_to_retrieve = [
p for p in pairs if _str_titles(*p) not in self.rel_dict
]
if pairs_to_retrieve:
for titles, rel in tagme.relatedness_title(pairs_to_retrieve):
self.rel_dict[_str_titles(*titles)] = rel
for p in pairs:
assert _str_titles(*p) in self.rel_dict
self.rel_dict.commit()
def entity_list(request):
import tagme
# Set the authorization token for subsequent calls.
tagme.GCUBE_TOKEN = "a5a377c1-1bd0-47b9-907a-75b1cdacb1d9-843339462"
db_post = PostTitle.objects.filter(user_id=request.user.id,
search_date_interval=val2(),
search_word=val(),
date=date.today().strftime("%b-%d-%Y"))
file1 = db_post.values_list('title', flat=True)
# file1 = open('file.txt', 'r', encoding='utf-8')
entity_title_list = []
# Using for loop
for line in file1:
lunch_annotations = tagme.annotate(line)
# Print annotations with a score higher than 0.1
for ann in lunch_annotations.get_annotations(min_rho=0.35):
entity_title_list.append(ann.entity_title)
# Closing files
# file1.close()
entity_title_list = list(dict.fromkeys(entity_title_list))
relation_degree_list = []
relation_pair_list = []
relation_list = [
"ALL RELATIONS BETWEEN ENTITIES ARE LISTED BELOW", "\n", "\n"
]
for i in range(0, len(entity_title_list)):
for j in range(i + 1, len(entity_title_list)):
rels = tagme.relatedness_title(
(entity_title_list[i], entity_title_list[j]))
relation_degree = rels.relatedness[0].rel
# print(f"{entity_title_list[i]} ve {entity_title_list[j]} {relation_degree}")
# print("\n")
if relation_degree != 0.0:
relation_pair_list.append(
(entity_title_list[i], entity_title_list[j]))
relation_degree_list.append(relation_degree)
pairs = entity_title_list[i] + " " + entity_title_list[j]
relation_list.append(pairs)
relation_list.append("\t")
relation_list.append(relation_degree)
relation_list.append("\n")
print(entity_title_list[i])
print("Kalan entity sayısı: ", len(entity_title_list) - (i + 1))
print("done")
return HttpResponse(relation_list, content_type="text/plain")
def tagme_matching(trend_one, trend_two):
matches = []
trend_one_processed = text_processing(trend_one, keep_spaces=True)
trend_two_processed = text_processing(trend_two, keep_spaces=True)
for keyword_one in trend_one_processed:
for keyword_two in trend_two_processed:
relations = tagme.relatedness_title(
(keyword_one['processed'], keyword_two['processed']))
print('Comparing ' + keyword_one['processed'] + ' and ' +
keyword_two['processed'])
if relations.relatedness[0].rel and int(
relations.relatedness[0].rel) > 0:
matches.append(keyword_one['original'])
if len(matches) == 0: return 'No matches'
return matches