def determine_matching_similarity(question_word, candidate, is_question_entity=False): if is_question_entity: matching_similarity = question_word['link_probability'] return matching_similarity else: matching_similarity = spacy.similarity_word2vec(question_word, candidate['label']) return matching_similarity
def determine_matching_similarity(question_word, candidate, is_question_entity=False): if is_question_entity: matching_similarity = question_word['link_probability'] return matching_similarity else: if not candidate['label']: return 0 label = wd.wikidata_id_to_label(candidate['label']) matching_similarity = spacy.similarity_word2vec(question_word, label) return matching_similarity
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