def build_candidate_priority_queue_one_entity(entity_id):
candidate_priority_queue_one_entity = []
statements = wd.get_all_statements_of_entity(entity_id)
# error handling
if not statements:
return []
for statement in statements:
# entity is the object of the statement
if entity_id == statement['object']['id']:
entity_label = wd.wikidata_id_to_label(statement['entity']['id'])
predicate_label = wd.wikidata_id_to_label(statement['predicate']['id'])
candidate_priority_queue_one_entity.append({'type': 'predicate', 'predicate': statement['predicate']['id'], 'label': predicate_label, 'statement': statement})
candidate_priority_queue_one_entity.append({'type': 'entity', 'entity': statement['entity']['id'], 'label': entity_label, 'statement': statement})
# entity is the subject of the statement
else:
object_label = wd.wikidata_id_to_label(statement['object']['id'])
predicate_label = wd.wikidata_id_to_label(statement['predicate']['id'])
candidate_priority_queue_one_entity.append({'type': 'predicate', 'predicate': statement['predicate']['id'], 'label': predicate_label, 'statement': statement})
candidate_priority_queue_one_entity.append({'type': 'entity', 'entity': statement['object']['id'], 'label': object_label, 'statement': statement})
# include the qualifiers
if statement['qualifiers']:
for qualifier in statement['qualifiers']:
qualifier_object_label = wd.wikidata_id_to_label(qualifier['qualifier_object']['id'])
qualifier_predicate_label = wd.wikidata_id_to_label(qualifier['qualifier_predicate']['id'])
candidate_priority_queue_one_entity.append({'type': 'qualifier_object', 'qualifier_object': qualifier['qualifier_object']['id'], 'label': qualifier_object_label, 'statement': statement})
candidate_priority_queue_one_entity.append({'type': 'qualifier_predicate', 'qualifier_predicate': qualifier['qualifier_predicate']['id'], 'label': qualifier_predicate_label, 'statement': statement})
return candidate_priority_queue_one_entity
def answer_complete_question(question, tagmeToken):
entities = tagme_get_all_entities(question, tagmeToken)
highest_matching_similarity = -1
for entity in entities:
shortened_question = string.shorten_question_for_predicate_similarity(question, entity['spot'])
statements = wd.get_all_statements_of_entity(entity['wikidata_id'])
for statement in statements:
# no identifier predicates
if statement['predicate']['id'] in identifier_predicates:
continue
predicate_label = wd.wikidata_id_to_label(statement['predicate']['id'])
matching_similarity = spacy.similarity_word2vec(predicate_label, shortened_question) * entity['link_probability']
if highest_matching_similarity == -1 or matching_similarity > highest_matching_similarity:
answer = statement['entity']['id'] if statement['object']['id'] == entity['wikidata_id'] else statement['object']['id']
context = {'entity': {'id': entity['wikidata_id']}, 'predicate': {'id': statement['predicate']['id']}, 'object': {'id': answer}}
result = {'context': context, 'answers': [{'answer': answer, 'rank': 1}] }
highest_matching_similarity = matching_similarity
return result
