def question_answer_hub(self, question_str):
"""
问答总控，基于aiml构建问题匹配器
:param question_str:问句输入
:return:
"""
g = rdfPrepare.load_graph()
question_replaced, entity_dict = entityMatch2.match_and_replace_all(question_str,g)
print(entity_dict)
#navi_g = rdfPrepare.load_navi_graph()
#for room in g.subjects(RDF.type, rdflib.URIRef("http://www.libot.org/room")):
#print(room)
#for s, p, o in g:
# print((s, p, o))
#print(g.serialize(format='n3'))
#navi_question_replaced, navi_entity_dict = entityMatch2.match_and_replace_all(question_str, navi_g)
'''
if multiwheelUnit.get_value('business') == "办理读书卡":
if "answer" not in multiwheelUnit.get_value('step'):
question_replaced += "读卡"
else:
multiwheelUnit.set_value('business',None)
multiwheelUnit.set_value('step', None)
'''
#aiml_respons = self._aiml_kernal.respond(question_replaced)
aiml_respons = self._aiml_kernal.respond(question_replaced)
print(aiml_respons)
'''
if 'multiwheeltask_'in aiml_respons:
print("aiml_respons: ", str(aiml_respons))
graph_respons = rdfBotMul.task_response(aiml_respons, entity_dict, question_str, g)
return graph_respons
'''
if 'task_' in aiml_respons:
#if aiml_respons == 'task_room_pos':
# return
#graph_respons = rdfBot.task_response(aiml_respons, navi_entity_dict, question_str, navi_g)
#graph_respons = rdfBot.task_response(aiml_respons, test_entity_dict, question_str, test_g)
#else:
graph_respons = rdfBot.task_response(aiml_respons,entity_dict,question_str,g)
return graph_respons
else:
return aiml_respons
def question_answer_hub(self, question_str):
"""
问答总控，基于aiml构建问题匹配器
:param question_str:问句输入
:return:
"""
g = rdfPrepare.load_graph()
question_replaced, entity_dict = entityMatch2.match_and_replace_all(
question_str, g)
# question_replaced, entity_dict = entityMatch.match_and_replace_all(question_str)
arr = []
if len(entity_dict['room']) > 0:
for i in entity_dict['room']:
if len(i) == 0:
continue
index = question_str.find(i[0])
arr.append(index)
# print(arr)
arr_index = np.argsort(np.array(arr))
# print(arr_index)
entity_dict2 = []
for i in entity_dict['room']:
if len(i) == 0:
continue
entity_dict2.append(i)
for i in range(len(entity_dict['room'])):
if len(entity_dict['room'][i]) == 0:
continue
# print(arr_index[i],entity_dict2[arr_index[i]])
entity_dict['room'][i] = entity_dict2[arr_index[i]]
aiml_respons = self._aiml_kernal.respond(question_replaced)
if 'task_' in aiml_respons:
print("aiml_respons: ", str(aiml_respons))
print("entity_dict: ", str(entity_dict))
graph_respons = rdfBot.task_response(aiml_respons, entity_dict, g)
return graph_respons
else:
return aiml_respons
question_str = question_str.replace(
varname, replace_entity_mark['floor'])
return question_str, entities
@classmethod
def match_and_replace_all(cls, question_str, graph):
"""
对问句进行所有类型实体匹配与替换
:param question_str:
:return:
"""
entity_dict = {} #分类存放匹配到的各类实体
var_list = DictMatch2.var_dict_list(graph)
question_str, entity_list = cls.room_dict_match(
question_str, var_list, graph)
entity_dict['room'] = entity_list
question_str, entity_list = cls.resource_dict_match(
question_str, var_list, graph)
entity_dict['res'] = entity_list
question_str, entity_list = cls.floor_dict_match(
question_str, var_list, graph)
entity_dict['floor'] = entity_list
return question_str, entity_dict
if __name__ == "__main__":
g = rdfPrepare.load_graph()
question_str, entity_list = entityMatch2.match_and_replace_all(
"日本出版物文库阅览室在总馆南区四层吗", g)
print(question_str)
print(entity_list)
def question_answer_hub(self, question_str):
"""
问答总控，基于aiml构建问题匹配器
:param question_str:问句输入
:return:
"""
g = rdfPrepare.load_graph()
question_replaced, entity_dict = entityMatch2.match_and_replace_all(
question_str, g)
#print(question_replaced,entity_dict)
navi_g = rdfPrepare.load_navi_graph()
navi_question_replaced, navi_entity_dict = entityMatch2.match_and_replace_all(
question_str, navi_g)
#print(navi_question_replaced, navi_entity_dict)
# question_replaced, entity_dict = entityMatch.match_and_replace_all(question_str)
'''
arr = []
if len(entity_dict['room']) > 0:
for i in entity_dict['room']:
if len(i) == 0:
continue
index = question_str.find(i[0])
arr.append(index)
# print(arr)
arr_index = np.argsort(np.array(arr))
# print(arr_index)
entity_dict2 = []
for i in entity_dict['room']:
if len(i) == 0:
continue
entity_dict2.append(i)
for i in range(len(entity_dict['room'])):
if len(entity_dict['room'][i]) == 0:
continue
# print(arr_index[i],entity_dict2[arr_index[i]])
entity_dict['room'][i] = entity_dict2[arr_index[i]]
'''
if multiwheelUnit.get_value('business') == "办理读书卡":
if "answer" not in multiwheelUnit.get_value('step'):
question_replaced += "读卡"
else:
multiwheelUnit.set_value('business', None)
multiwheelUnit.set_value('step', None)
aiml_respons = self._aiml_kernal.respond(question_replaced)
if 'multiwheeltask_' in aiml_respons:
print("aiml_respons: ", str(aiml_respons))
# print("entity_dict: ", str(entity_dict))
graph_respons = rdfBotMul.task_response(aiml_respons, entity_dict,
question_str, g)
return graph_respons
elif 'task_' in aiml_respons:
print("aiml_respons: ", str(aiml_respons))
#print("entity_dict: ", str(entity_dict))
if aiml_respons == 'task_room_pos':
graph_respons = rdfBot.task_response(aiml_respons,
navi_entity_dict,
question_str, navi_g)
else:
graph_respons = rdfBot.task_response(aiml_respons, entity_dict,
question_str, g)
return graph_respons
else:
return aiml_respons