def test_get_most_similar_top_n_entity(self):
sim_matrix=np.matrix(np.array([[1, 2, 3, 4],[6,6,1,5]]))
one_hot_result= MatrixCalculation.get_most_similar_top_n_entity_as_matrix(top_n=3,s_e_similarity_matrix=sim_matrix)
print(one_hot_result)
one_hot_result = MatrixCalculation.get_most_similar_top_n_entity_as_matrix(top_n=5,
s_e_similarity_matrix=sim_matrix)
print(one_hot_result)
def sort_sentence_by_build_average_graph_vector_for_query(self, question, sentence_list, entity_list,
weight_context_sim=0.5, weight_graph_sim=0.5
):
self._logger.info(
"run sort_sentence_by_build_average_graph_vector_for_query get sentence_list=%d" % len(sentence_list))
kg_models = self.kg_models
question_context_vec = kg_models.get_question_entity_vector(question)
entity_vec_list, entity_graph_vec_list = self.kg_models.get_vectors_for_entity_list(entity_list)
sentence_vec_list, sentence_graph_vec_list = self.kg_models.get_vectors_for_entity_list(sentence_list)
entity_list, entity_vec_list, entity_graph_vec_list = self.remove_the_not_related_entity(entity_graph_vec_list,
entity_list,
entity_vec_list,
question_context_vec)
query_graph_vector = kg_models.get_question_graph_vector_by_average_all_entities(
question=question,
entity_graph_vec_list=entity_graph_vec_list)
qs_context_sim = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(question_context_vec,
sentence_vec_list)
qs_graph_sim = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(query_graph_vector,
sentence_graph_vec_list)
qs_context_sim = weight_context_sim * qs_context_sim
qs_graph_sim = weight_graph_sim * qs_graph_sim
qs_sim = qs_context_sim + qs_graph_sim
qs_sim = qs_sim.tolist()[0]
qs_context_sim = qs_context_sim.tolist()[0]
qs_graph_sim = qs_graph_sim.tolist()[0]
for sum_sim, sentence, context_sim, graph_sim in zip(qs_sim, sentence_list, qs_context_sim, qs_graph_sim):
sentence["qs_sim"] = sum_sim
sentence["qs_context_sim"] = context_sim
sentence["qs_graph_sim"] = graph_sim
result = []
for sentence in sentence_list:
result.append({
"kg_id": self.defaultAccessor.get_id_for_node(sentence),
"sentence_id": sentence["sentence_id"],
"text": sentence["sentence_text"],
"sentence_type": sentence["sentence_type_code"],
"qs_sim": sentence["qs_sim"],
"qs_context_sim": sentence["qs_context_sim"],
"qs_graph_sim": sentence["qs_graph_sim"]
})
self._logger.info("run sort_sentence_by_build_average_graph_vector_for_query get result num=%d" % len(result))
result.sort(key=lambda k: (k.get('qs_sim', 0)), reverse=True)
return result
def sort_sentence_by_entities_for_graph_similarity_as_bridge(self, question,
sentence_list,
entity_list,
weight_context_sim=0.5,
weight_graph_sim=0.5
):
self._logger.info(
"run sort_sentence_by_entities_for_graph_similarity_as_bridge get result=%d" % len(sentence_list))
question_context_vec = self.kg_models.get_question_entity_vector(question)
entity_vec_list, entity_graph_vec_list = self.kg_models.get_vectors_for_entity_list(entity_list)
sentence_vec_list, sentence_graph_vec_list = self.kg_models.get_vectors_for_entity_list(sentence_list)
qe_sim_np = np.ones((1, len(entity_list)))
qe_sim_np = qe_sim_np / qe_sim_np.sum()
qs_context_sim = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(question_context_vec,
sentence_vec_list)
kg_graph_sim = MatrixCalculation.compute_cossin_for_matrix_to_matrix_normalize(entity_graph_vec_list,
sentence_graph_vec_list)
qs_context_sim = weight_context_sim * qs_context_sim
qs_graph_sim = weight_graph_sim * qe_sim_np * kg_graph_sim
qs_sim = qs_context_sim + qs_graph_sim
qs_sim = qs_sim.tolist()[0]
qs_context_sim = qs_context_sim.tolist()[0]
qs_graph_sim = qs_graph_sim.tolist()[0]
for sum_sim, sentence, context_sim, graph_sim in zip(qs_sim, sentence_list, qs_context_sim, qs_graph_sim):
sentence["qs_sim"] = sum_sim
sentence["qs_context_sim"] = context_sim
sentence["qs_graph_sim"] = graph_sim
result = []
for sentence in sentence_list:
result.append({
"kg_id": self.defaultAccessor.get_id_for_node(sentence),
"sentence_id": sentence["sentence_id"],
"sentence_type": sentence["sentence_type_code"],
"text": sentence["sentence_text"],
"qs_sim": sentence["qs_sim"],
"qs_context_sim": sentence["qs_context_sim"],
"qs_graph_sim": sentence["qs_graph_sim"]
})
self._logger.info("run sort_sentence_by_entities_as_bridge get result num=%d" % len(result))
result.sort(key=lambda k: (k.get('qs_sim', 0)), reverse=True)
print("sorted result")
for t in result:
print("test sort", t)
print(result[:100])
return result
def calculate_matrix(selected_data_list, vector_map):
vector_list = []
for i in range(0, len(selected_data_list)):
paragraph_id = str(selected_data_list[i]["paragraph_id"])
vector = vector_map[paragraph_id]
vector_list.append(vector)
return MatrixCalculation.cosine_matrix(vector_list, vector_list)
def get_graph_similarity_by_average_entity_graph_vector(
self, entity_graph_vec_list, question, sentence_graph_vec_list):
query_graph_vector = self.kg_models.get_question_graph_vector_by_average_all_entities(
question=question, entity_graph_vec_list=entity_graph_vec_list)
qs_graph_sim = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(
query_graph_vector, sentence_graph_vec_list)
return qs_graph_sim
def fill_each_entity_with_similary_to_question(self, question_context_vec):
entity_context_vec_list = self.get_entity_context_list()
qe_sim_np = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(question_context_vec,
entity_context_vec_list)
for entity_info, sim in zip(self.api_info_list, qe_sim_np.getA()[0]):
entity_info.qe_sim = sim
def get_graph_similarity_average_entity_graph_vector_similarity(self, entity_graph_vec_list, question,
sentence_graph_vec_list):
# query_graph_vector = self.kg_models.get_question_graph_vector_by_average_all_entities(
# question=question,
# entity_graph_vec_list=entity_graph_vec_list)
qs_graph_sim = MatrixCalculation.compute_cossin_for_matrix_to_matrix_normalize(sentence_graph_vec_list,
entity_graph_vec_list)
return np.mean(qs_graph_sim, axis=1)
def get_question_graph_vector_by_semantic_weight_all_entities(self, question_context_vec, entity_context_vec_list,
entity_graph_vec_list):
qe_sim_np = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(question_context_vec,
entity_context_vec_list)
qe_sim_np = qe_sim_np / qe_sim_np.sum()
question_graph_vec = (qe_sim_np * np.matrix(entity_graph_vec_list)).getA()[0]
return question_graph_vec
def get_query_to_sentence_graph_sim_by_select_top_enttity(self, entity_graph_vec_list, entity_list, entity_vec_list,
sentence_graph_vec_list, sentence_vec_list):
# kg_se_graph_sim = MatrixCalculation.compute_cossin_for_matrix_to_matrix_normalize(sentence_graph_vec_list,
# entity_graph_vec_list,
# )
kg_se_context_sim = MatrixCalculation.compute_cossin_for_matrix_to_matrix_normalize(
sentence_vec_list,
entity_vec_list)
# TODO
# kg_se_sim = 0.5 * kg_se_graph_sim + 0.5 * kg_se_context_sim
kg_se_sim = kg_se_context_sim
print("final entity list", len(entity_list), entity_list)
select_linking_entity_num = min(5, len(entity_list))
onehot_maxsim_se_matrix = MatrixCalculation.get_most_similar_top_n_entity_as_matrix(
top_n=select_linking_entity_num, s_e_similarity_matrix=kg_se_sim)
s_query_graph_vec_matrix = onehot_maxsim_se_matrix * np.matrix(
entity_graph_vec_list) / select_linking_entity_num
qs_graph_sim = MatrixCalculation.compute_cossin_for_one_to_one_in_two_list_normalize(sentence_graph_vec_list,
s_query_graph_vec_matrix.getA())
return qs_graph_sim
def remove_the_not_related_entity_by_only_save_one_for_each(
self, entity_graph_vec_list, entity_list, entity_vec_list,
question_context_vec, qa_info_manager):
chunk_to_related_entity_list_map = qa_info_manager.keyword_2_entitynodemap
qe_sim_np = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(
question_context_vec, entity_vec_list)
entity_info_sumary_list = []
for (entity, sim, entity_vec,
entity_graph_vec) in zip(entity_list,
qe_sim_np.getA()[0], entity_vec_list,
entity_graph_vec_list):
# print("after first removing sim=", sim, "entity=", entity)
entity_info_sumary_list.append({
"entity": entity,
"sim": sim,
"entity_vec": entity_vec,
"entity_graph_vec": entity_graph_vec
})
entity_info_sumary_list.sort(key=lambda k: (k.get('sim', 0)),
reverse=True)
valid_word_set = set([])
word_to_related_entity_list_map = {}
for chunk, related_entity_list in chunk_to_related_entity_list_map.items(
):
word = chunk
if word not in valid_word_set:
valid_word_set.add(word)
word_to_related_entity_list_map[word] = related_entity_list
else:
word_to_related_entity_list_map[word].extend(
related_entity_list)
# clean_entity_info_list = self.get_clean_entity_for_each_word_by_max_similarity(entity_info_sumary_list,
# word_to_related_entity_list_map)
#
clean_entity_info_list = self.get_clean_entity_for_each_word_by_max_n_similarity(
entity_info_sumary_list, word_to_related_entity_list_map)
new_entity_list = []
new_entity_graph_vec_list = []
new_entity_vec_list = []
for entity_info_sumary in clean_entity_info_list:
new_entity_list.append(entity_info_sumary["entity"])
new_entity_graph_vec_list.append(
entity_info_sumary["entity_graph_vec"])
new_entity_vec_list.append(entity_info_sumary["entity_vec"])
# print("final save sim=", entity_info_sumary["sim"], "entity=", entity_info_sumary["entity"])
return new_entity_list, new_entity_vec_list, new_entity_graph_vec_list
def sort_api_by_select_part_entity_as_bridge(
self,
question,
qa_info_manager,
weight_context_sim=0.6,
weight_graph_sim=0.4,
):
api_info_collection = qa_info_manager.get_api_info_collection()
api_entity_list = api_info_collection.get_entity_list()
api_context_vec_list = api_info_collection.get_entity_context_list()
api_graph_vec_list = api_info_collection.get_entity_graph_list()
question_context_vec = self.kg_models.get_question_entity_vector(
question)
entity_info_collection = qa_info_manager.get_top_related_entity_info_collection(
)
entity_list = entity_info_collection.get_entity_list()
entity_context_vec_list = entity_info_collection.get_entity_context_list(
)
entity_graph_vec_list = entity_info_collection.get_entity_graph_list()
qe_context_sim = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(
question_context_vec, api_context_vec_list)
# todo:change to the average graph similarity
qe_graph_sim = self.get_graph_similarity_by_average_entity_graph_vector(
entity_graph_vec_list, question, api_graph_vec_list)
# qe_graph_sim = self.get_query_to_sentence_graph_sim_by_select_top_enttity(entity_graph_vec_list, entity_list,
# entity_context_vec_list,
# api_graph_vec_list,
# api_context_vec_list)
#
qe_context_sim = weight_context_sim * qe_context_sim
qe_graph_sim = weight_graph_sim * qe_graph_sim
qe_sim = qe_context_sim + qe_graph_sim
qe_sim = qe_sim.tolist()[0]
qe_context_sim = qe_context_sim.tolist()[0]
qe_graph_sim = qe_graph_sim.tolist()[0]
for api_info, sum_sim, sentence, context_sim, graph_sim in zip(
qa_info_manager.get_api_info_collection().api_info_list,
qe_sim, api_entity_list, qe_context_sim, qe_graph_sim):
api_info.qe_sim = sum_sim
api_info.qe_context_sim = context_sim
api_info.qe_graph_sim = graph_sim
qa_info_manager.get_api_info_collection().sort_by_qe_sim()
return qa_info_manager
def sort_sentences_for_one_api_class(
self,
question,
qa_info_manager,
sentence_info_collection,
weight_context_sim=0.6,
weight_graph_sim=0.4,
):
sentence_info_collection.init_vectors(self.kg_models)
sentence_list = sentence_info_collection.get_entity_list()
sentence_vec_list = sentence_info_collection.get_entity_context_list()
sentence_graph_vec_list = sentence_info_collection.get_entity_graph_list(
)
question_context_vec = self.kg_models.get_question_entity_vector(
question)
entity_info_collection = qa_info_manager.get_top_related_entity_info_collection(
)
entity_graph_vec_list = entity_info_collection.get_entity_graph_list()
qs_context_sim = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(
question_context_vec, sentence_vec_list)
qs_graph_sim = self.get_graph_similarity_by_average_entity_graph_vector(
entity_graph_vec_list, question, sentence_graph_vec_list)
qs_context_sim = weight_context_sim * qs_context_sim
qs_graph_sim = weight_graph_sim * qs_graph_sim
qs_sim = qs_context_sim + qs_graph_sim
qs_sim = qs_sim.tolist()[0]
qs_context_sim = qs_context_sim.tolist()[0]
qs_graph_sim = qs_graph_sim.tolist()[0]
for sentence_info, sum_sim, sentence, context_sim, graph_sim in zip(
sentence_info_collection.sentence_info_list, qs_sim,
sentence_list, qs_context_sim, qs_graph_sim):
sentence_info.qs_sim = sum_sim
sentence_info.qs_context_sim = context_sim
sentence_info.qs_graph_sim = graph_sim
sentence_info_collection.sort_by_qs_sim()
return sentence_info_collection
def remove_the_not_related_entity(self, entity_graph_vec_list, entity_list, entity_vec_list, question_context_vec):
qe_sim_np = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(question_context_vec,
entity_vec_list)
print("qeustion to entity similary")
new_entity_list = []
new_entity_vec_list = []
new_entity_graph_vec_list = []
qe_sim_clean = []
for (entity, sim, entity_vec, entity_graph_vec) in zip(entity_list, qe_sim_np.getA()[0], entity_vec_list,
entity_graph_vec_list):
print("sim=", sim, "entity=", entity)
if sim > MIN_RELATED_ENTITY_SIMILARITY:
print("adding ", entity)
new_entity_list.append(entity)
new_entity_vec_list.append(entity_vec)
new_entity_graph_vec_list.append(entity_graph_vec)
qe_sim_clean.append(sim)
entity_list = new_entity_list
entity_vec_list = new_entity_vec_list
entity_graph_vec_list = new_entity_graph_vec_list
new_entity_list = []
new_entity_vec_list = []
new_entity_graph_vec_list = []
entity_info_sumary_list = []
for (entity, sim, entity_vec, entity_graph_vec) in zip(entity_list, qe_sim_clean, entity_vec_list,
entity_graph_vec_list):
print("after first removing sim=", sim, "entity=", entity)
entity_info_sumary_list.append({"entity": entity,
"sim": sim,
"entity_vec": entity_vec,
"entity_graph_vec": entity_graph_vec
})
entity_info_sumary_list.sort(key=lambda k: (k.get('sim', 0)), reverse=True)
api_class_name_set = set([])
new_entity_info_sumary_list = []
for entity_info_sumary in entity_info_sumary_list:
if entity_info_sumary["entity"].has_label("api"):
qualified_name = entity_info_sumary["entity"]["qualified_name"]
if qualified_name in api_class_name_set:
continue
if "(" in qualified_name:
simple_name = qualified_name.split("(")[0]
class_name = ".".join(simple_name.split(".")[:-1])
if class_name in api_class_name_set:
continue
else:
api_class_name_set.add(class_name)
new_entity_info_sumary_list.append(entity_info_sumary)
else:
api_class_name_set.add(qualified_name)
new_entity_info_sumary_list.append(entity_info_sumary)
else:
new_entity_info_sumary_list.append(entity_info_sumary)
for entity_info_sumary in new_entity_info_sumary_list:
new_entity_list.append(entity_info_sumary["entity"])
new_entity_graph_vec_list.append(entity_info_sumary["entity_graph_vec"])
new_entity_vec_list.append(entity_info_sumary["entity_vec"])
print("final save sim=", entity_info_sumary["sim"], "entity=", entity_info_sumary["entity"])
return new_entity_list, new_entity_vec_list, new_entity_graph_vec_list
def sort_sentence_by_select_part_entity_as_bridge(self, question,
qa_info_manager,
# sentence_list,
# entity_list,
weight_context_sim=0.6,
weight_graph_sim=0.4,
# chunk_to_related_entity_list_map=None,
):
self._logger.info(
"run sort part entity result=%d" % qa_info_manager.get_sentence_size())
print("entity for node")
qa_info_manager.print_entities()
print("sentence for node")
# qa_info_manager.print_sentences()
entity_info_collection = qa_info_manager.get_entity_info_collection()
sentence_info_collection = qa_info_manager.get_sentence_info_collection()
entity_info_collection.init_vectors(self.kg_models)
sentence_info_collection.init_vectors(self.kg_models)
sentence_list = sentence_info_collection.get_entity_list()
entity_vec_list = entity_info_collection.get_entity_context_list()
entity_graph_vec_list = entity_info_collection.get_entity_graph_list()
entity_list = entity_info_collection.get_entity_list()
sentence_vec_list = sentence_info_collection.get_entity_context_list()
sentence_graph_vec_list = sentence_info_collection.get_entity_graph_list()
question_context_vec = self.kg_models.get_question_entity_vector(question)
entity_list, entity_vec_list, entity_graph_vec_list = self.get_top_related_entity_info_list(
question_context_vec=question_context_vec, qa_info_manager=qa_info_manager)
# entity_list, entity_vec_list, entity_graph_vec_list = self.remove_the_not_related_entity_by_only_save_one_for_each(
# entity_graph_vec_list=entity_graph_vec_list, entity_vec_list=entity_vec_list, entity_list=entity_list,
# question_context_vec=question_context_vec,
# qa_info_manager=qa_info_manager
#
# )
qs_context_sim = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(question_context_vec,
sentence_vec_list)
# todo:change to the average graph similarity
# qs_graph_sim = self.get_graph_similarity_by_average_entity_graph_vector(entity_graph_vec_list, question,
# sentence_graph_vec_list)
qs_graph_sim = self.get_query_to_sentence_graph_sim_by_select_top_enttity(entity_graph_vec_list, entity_list,
entity_vec_list,
sentence_graph_vec_list,
sentence_vec_list)
qs_context_sim = weight_context_sim * qs_context_sim
qs_graph_sim = weight_graph_sim * qs_graph_sim
qs_sim = qs_context_sim + qs_graph_sim
qs_sim = qs_sim.tolist()[0]
qs_context_sim = qs_context_sim.tolist()[0]
qs_graph_sim = qs_graph_sim.tolist()[0]
for sum_sim, sentence, context_sim, graph_sim in zip(qs_sim, sentence_list, qs_context_sim, qs_graph_sim):
sentence["qs_sim"] = sum_sim
sentence["qs_context_sim"] = context_sim
sentence["qs_graph_sim"] = graph_sim
result = []
for sentence in sentence_list:
result.append({
"kg_id": self.defaultAccessor.get_id_for_node(sentence),
"sentence_id": sentence["sentence_id"],
"sentence_type": sentence["sentence_type_code"],
"text": sentence["sentence_text"],
"qs_sim": sentence["qs_sim"],
"qs_context_sim": sentence["qs_context_sim"],
"qs_graph_sim": sentence["qs_graph_sim"]
})
self._logger.info("run sort_sentence_by_entities_as_bridge get result num=%d" % len(result))
result.sort(key=lambda k: (k.get('qs_sim', 0)), reverse=True)
print(result[:100])
return result
def sort_sentence_by_select_part_entity_as_bridge(
self,
question,
qa_info_manager,
weight_context_sim=0.6,
weight_graph_sim=0.4,
):
self._logger.info("run sort part entity result=%d" %
qa_info_manager.get_sentence_size())
sentence_info_collection = qa_info_manager.get_sentence_info_collection(
)
sentence_info_collection.init_vectors(self.kg_models)
sentence_list = sentence_info_collection.get_entity_list()
sentence_vec_list = sentence_info_collection.get_entity_context_list()
sentence_graph_vec_list = sentence_info_collection.get_entity_graph_list(
)
question_context_vec = self.kg_models.get_question_entity_vector(
question)
entity_info_collection = qa_info_manager.get_top_related_entity_info_collection(
)
entity_list = entity_info_collection.get_entity_list()
entity_context_vec_list = entity_info_collection.get_entity_context_list(
)
entity_graph_vec_list = entity_info_collection.get_entity_graph_list()
# entity_list, entity_vec_list, entity_graph_vec_list = self.remove_the_not_related_entity_by_only_save_one_for_each(
# entity_graph_vec_list=entity_graph_vec_list, entity_vec_list=entity_vec_list, entity_list=entity_list,
# question_context_vec=question_context_vec,
# qa_info_manager=qa_info_manager
#
# )
qs_context_sim = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(
question_context_vec, sentence_vec_list)
# todo:change to the average graph similarity
qs_graph_sim = self.get_graph_similarity_by_average_entity_graph_vector(
entity_graph_vec_list, question, sentence_graph_vec_list)
# qs_graph_sim = self.get_query_to_sentence_graph_sim_by_select_top_enttity(entity_graph_vec_list, entity_list,
# entity_vec_list,
# sentence_graph_vec_list,
# sentence_vec_list)
qs_context_sim = weight_context_sim * qs_context_sim
qs_graph_sim = weight_graph_sim * qs_graph_sim
qs_sim = qs_context_sim + qs_graph_sim
qs_sim = qs_sim.tolist()[0]
qs_context_sim = qs_context_sim.tolist()[0]
qs_graph_sim = qs_graph_sim.tolist()[0]
for sentence_info, sum_sim, sentence, context_sim, graph_sim in zip(
sentence_info_collection.sentence_info_list, qs_sim,
sentence_list, qs_context_sim, qs_graph_sim):
sentence_info.qs_sim = sum_sim
sentence_info.qs_context_sim = context_sim
sentence_info.qs_graph_sim = graph_sim
sentence_info_collection.sort_by_qs_sim()
return qa_info_manager