-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathQA_init.py
700 lines (670 loc) · 30.2 KB
/
QA_init.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
import os
import re
import random
from collections import defaultdict
from KB import Query
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
from b4k.bert4keras.questionTypeClass import QuestionTypeClass
from simbert.simbert_base import generateSimSentence
import time
from b4k.bert4keras.entityRecognize import NER
import requests
try:
import simplejson as json
except:
import json
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
dir_path = os.getcwd()
class AnswerByOwnthink(object):
def __init__(self,):
self.ques = ''
self.ans_list = [] # 存放一次查询的结果
self.ner = NER() # 实体识别
self.query = Query() # neo4j查询
self.sim = generateSimSentence() # 相似度计算
self.quesClass = QuestionTypeClass() # 分类模型，对问题进行分类
self.prop_dict = dict() # 存放识别得到的属性名
self.searchPath = 'none->none' # 程序最终查询路径
self.attr_dict = defaultdict(list) # 用以处理爸爸、妈妈、父亲相似度计算短板问题，存放映射
self.stopFile = dir_path + '/src/qa/data/stopwords.txt' # 加载停用词文件
self.attrFile = dir_path + '/src/qa/data/attr_mapping.txt' # 映射文件
self.fuzzyFile = dir_path + '/src/qa/data/fuzzy.txt' # 不完全实体的映射匹配
self.stopWords = [line.strip() for line in open(self.stopFile,'r',encoding='utf-8').readlines()] # 构建停用词
self.fuzzy_dict = dict() # 模糊匹配字典
self.merge = MergeProperty() # 属性融合
def searchQustion(self,question:str):
entity_dict,self.prop_dict = self.getEntProp2dict(question) # 获取实体和属性识别结果，根据SENT、OENT排序
question_type = self.quesClass.question_type_predict(question) # ['SP_O','SPP_O','SO_P','PO_S','OP_S']，进行分类
entity_len = len(entity_dict)
prop_len = len(self.prop_dict)
self.ques = self.removeStopWordsList(question) # 去除停用词
if entity_len == 0: # 实体不存在的情况
res = '未识别到实体，请重新输入您的问题'
return res,'none->none',0.00,question_type
# sp_o查询
if question_type == '0':
entity = list(entity_dict.keys())[0]
if prop_len == 1:
prop = list(self.prop_dict.keys())[0]
res,searchPath,sim = self.SPO(entity,prop)
if res!= 'none': # sp_o直接查询得到结果，直接返回
return res,searchPath,sim,question_type
else: # sp_o得不到结果，则使用相似度匹配法
res,searchPath,sim = self.O_By_SP(entity)
return res,searchPath,sim,question_type
else: # 找不到属性，则使用相似度匹配法
res,searchPath,sim = self.O_By_SP(entity)
return res,searchPath,sim,question_type
# spp_o查询
elif question_type == '1':
entity = list(entity_dict.keys())[0]
if prop_len == 2: # spp_o查询
if int(list(self.prop_dict.values())[0]) < int(list(self.prop_dict.values())[1]): # 判断两个属性的前后顺序
prop_01 = list(self.prop_dict.keys())[0]
prop_02 = list(self.prop_dict.keys())[1]
else:
prop_01 = list(self.prop_dict.keys())[1]
prop_02 = list(self.prop_dict.keys())[0]
res,searchPath,sim = self.SPPO(entity,prop_01,prop_02)
if res !='none':
return res,searchPath,sim,question_type
else:
res,searchPath,sim = self.O_By_SPP(entity) # 直接查询无结果，使用相似度匹配算法
return res,searchPath,question_type
else:
res,searchPath,sim = self.O_By_SPP(entity) # 直接查询无结果，使用相似度匹配算法
return res,searchPath,sim,question_type
# SO_P关系查询
elif question_type == '2':
if entity_len == 2:
res,searchPath,sim = self.P_By_SO(entity_dict) # 关系查询，返回结果和查询路径
return res,searchPath,sim,question_type
else:
res = '识别到问题类型未SO->P，但实体数目低于或超过了2个，请重新输入。'
return res, 'none->none',0.00,question_type
# PO_S查询、 OP_S查询
elif question_type == '3' or question_type == '4':
entity = list(entity_dict.keys())[0]
if prop_len == 1:
prop = list(self.prop_dict.keys())[0]
res,searchPath,sim = self.POS(entity,prop)
if res!= 'none': # PO_S直接查询，若有结果，直接返回
return res,searchPath,sim,question_type
else: # PO_S无结果，使用相似度计算法进行
res,searchPath,sim = self.S_By_PO(entity)
return res,searchPath,sim,question_type
else:
res,searchPath,sim = self.S_By_PO(entity)
return res,searchPath,sim,question_type
## 给定实体和属性，查询属性值
def SPO(self,entity,prop):
print('搜索类型：SPO')
res = list((self.query.SP_O(entity,prop)).values()) # 直接通过实体名+属性值查询
ans = []
if res:
# print(res)
ans.append(','.join(res))
searchPath = entity + '->' + prop
sim_val = 1.00
return ans[0],searchPath,sim_val
else:
data_list_dict = self.query.Q_Z_ByEntity(entity) # 获取与实体相关的歧义关系列表[{ }]
if data_list_dict: # 歧义关系存在的情况
ans = ['none'] # 要返回的列表结果
searchPath = 'none->none'
sim_val = 0 # 相似度的值
data_info = self.merge.mergeProperty(data_list_dict)
entity_data_info_list = list(data_info.keys())
for ent in entity_data_info_list:
if prop in data_info[ent]:
ans[0]=','.join(data_info[ent][prop])
searchPath = ent + '->' + prop
sim_val = 1.00
return ans[0],searchPath,sim_val
return ans[0],searchPath,sim_val
else:
return 'none','none->none',0.00
# 给定S、P、P，查询O,多跳
def SPPO(self,entity,prop_01,prop_02):
print('搜索类型：SPPO')
ans_1,searchPath_01,sim_01 = self.SPO(entity,prop_01) # 单步查询
if ans_1 != 'none':
ans_2,searchPath_02,sim_02 = self.SPO(ans_1,prop_02) # 二次查询
if ans_2 != 'none':
searchPath = searchPath_01+'->'+searchPath_02
return ans_2,searchPath,sim_02
else:
ans_list = [ans_1,searchPath_01,sim_01]
self.ans_list = ans_list # 存放SPP_O的结果，做比较用
ans_2,searchPath_02,sim_02 = self.O_By_SPP(entity)
if ans_2 != 'none':
return ans_2,searchPath_02,sim_02
else:
return 'none','none->none',0.00
else:
ans,searchPath,sim = self.O_By_SP(entity)
ans_list = [ans,searchPath,sim]
self.ans_list = ans_list
if ans !='none':
ans_2,searchPath_02,sim_02 = self.O_By_SPP(entity)
if ans_2 != 'none':
return ans_2,searchPath_02,sim_02
else:
return 'none','none->none',0.00
else:
return 'none','none->none',0.00
# 给定P和O,查询S
def POS(self,entity,prop):
print('搜索类型：POS')
res = []
neo4j_ans = dict()
neo4j_ans = self.query.OP_S(prop,entity)
if neo4j_ans:
res = list(neo4j_ans.values()) # 根据属性和属性值，neo4j直接查询对应的实体
ans = []
if res:
temp_str = ','.join(res)
ans.append(temp_str)
searchPath = prop+ '->' + entity
sim_val = 1.00
return ans[0],searchPath,sim_val
else:
return 'none','none->none',0.00
# 通过相似度计算sp_o类型问题的查询
def O_By_SP(self,entity):
print('搜索类型：O_SP')
data_list_dict = self.query.Q_Z_ByEntity(entity) # 获取与实体相关的歧义关系列表[{ }]
self.attr_dict = self.getSimilarityAttr()
searchPath = 'none->none'
sim_val = 0.00
if data_list_dict: # 歧义关系存在的情况
ans = ['none'] # 要返回的列表结果
sim_val = 0.00 # 相似度的值
data_info = self.merge.mergeProperty(data_list_dict)
entity_data_info_list = list(data_info.keys())
for ent in entity_data_info_list:
prop_list = list(data_info[ent].keys())
ques = [self.ques]
for r_name in prop_list[1:]:
que = entity + r_name
ques.append(que)
sim_val_list = self.sim.gen_all_sim_value(ques) # 批量计算
temp_sim_val = max(sim_val_list)
comp_sim_val ='%.4f'%(float(temp_sim_val))
if float(comp_sim_val) > float(sim_val):
r_name_index = sim_val_list.index(temp_sim_val) + 1
r_name = prop_list[r_name_index]
ans[0] = ','.join(data_info[ent][r_name])
searchPath = ent + '->' + r_name
sim_val = comp_sim_val
print('相似问题顺序：%s,相似度%s'%(ques[r_name_index],sim_val))
return ans[0],searchPath,sim_val
else: # 歧义关系不存在
ans = ['none']
sim_val = 0.00
# entity_rela_dict = defaultdict(list) # 构造字典列表
S_P_list = self.query.S_P(entity)
rela_info = self.merge.merge_S_P_Property(S_P_list)
if rela_info:
ques = [self.ques]
prop_list = list(rela_info.keys())
for s_prop in prop_list:
que = entity + s_prop
ques.append(que)
sim_val_list = self.sim.gen_all_sim_value(ques)
temp_sim_val = max(sim_val_list)
comp_sim_val ='%.4f'%(float(temp_sim_val))
if float(comp_sim_val) > float(sim_val):
r_name_index = sim_val_list.index(temp_sim_val)
r_name = prop_list[r_name_index]
ans[0] = ','.join(rela_info[r_name])
searchPath = entity + '->' + r_name
sim_val = comp_sim_val
print('相似问题顺序：%s,相似度%s'%(ques[r_name_index+1],sim_val))
return ans[0] , searchPath,sim_val
else:
return '实体无关系词，查询结果：无','none->none',0.00
# 通过相似度计算spp_o类型问题的查询
def O_By_SPP(self,entity):
print('搜索类型：O_SPP')
ans = ['none']
searchPath = 'none'
res_01,searchPath_01,sim_01 = self.O_By_SP(entity)
self.ans_list = [res_01,searchPath_01,sim_01]
if float(self.ans_list[-1]) != 0.00:
S_P_list = self.query.S_P(self.ans_list[0])
rela_info = self.merge.merge_S_P_Property(S_P_list)
search_path_01 = self.ans_list[1].replace('->','')
search_path_01 = re.sub(u"\\[.*?\\]",'',search_path_01)
sim_val = 0
ques = [self.ques]
prop_list = list(rela_info.keys())
for sp_prop in prop_list:
que = search_path_01 + sp_prop
ques.append(que)
sim_val_list = self.sim.gen_all_sim_value(ques)
if not sim_val_list:
return 'none','none->none',0.00
temp_sim_val = max(sim_val_list)
comp_sim_val ='%.4f'%(float(temp_sim_val))
if float(comp_sim_val) > float(sim_val):
r_name_index = sim_val_list.index(temp_sim_val)
r_name = prop_list[r_name_index]
ans[0] = ','.join(rela_info[r_name])
searchPath = self.ans_list[1] + '->' + r_name
sim_val = comp_sim_val
# print('相似问题顺序：%s,相似度%s'%(ques[r_name_index+1],sim_val))
return ans[0],searchPath,sim_val
else:
return 'none','none->none', 0.00
# 查询实体的关系
def P_By_SO(self,entity_dict:dict):
print('搜索类型：P_SO')
entity_01,entity_02 = list(entity_dict.keys())[0],list(entity_dict.keys())[0]
for key,index in entity_dict.items():
if index =='SENT':
entity_01 = key
elif index == 'OENT':
entity_02 = key
sim_val = 0.00
data_list_dict = self.query.Q_Z_ByEntity(entity_01)
res = ''
# data_info = self.merge.mergeProperty(data_list_dict)
if data_list_dict:
for dict_data in data_list_dict:
# m_name = dict_data['m.name'] # 实体名称
r2_name = dict_data['r2.name'] # 关系名称
n2_name = dict_data['n2.name'] # 对应实体属性值
if n2_name == entity_02:
res = "{}:{}:{}".format(entity_01,r2_name,entity_02)
searchPath = dict_data['n.name'] + '-[r]-' +entity_02
sim_val = 1.00
break
if res !='':
return res, searchPath,sim_val
else:
res = self.query.P_By_SO(entity_01,entity_02)
if not res:
res,searchPath,sim_val = self.O_By_SP(entity_02)
else:
searchPath = res
sim_val = 1.00
return res, searchPath,sim_val
# 相似度计算，根据关系和实体查询实体
def S_By_PO(self,entity):
print('搜索类型：S_PO')
data_dict_list = self.query.OP_S_ByEntity(entity) # 先查找与实体相关的信息
sim_val = 0.00
ans = ['none']
if data_dict_list:
for data_dict in data_dict_list:
r_name = data_dict['r.name']
que = r_name + entity
temp_sim_val = self.sim.gen_sim_value(self.ques,que)
if float(temp_sim_val) > float(sim_val):
sim_val = temp_sim_val
ans[0] = data_dict['m.name']
searchPath = r_name + '->' + entity
# print('相似问题：%s，相似度:%s,查询路径：%s'%(que,sim_val,searchPath))
if float(temp_sim_val) == float(sim_val) and data_dict['m.name'] not in ans[0]:
ans[0] = ans[0] + ',' + data_dict['m.name']
searchPath = r_name + '->' + entity
return ans[0],searchPath,sim_val
else:
ans[0] = '未能查到实体%s的相关信息，请重新输入'%(entity)
return ans[0],'none->none', 0.00
# 返回实体和属性字典，实体以SENT,OENT作为键，实体名作为，属性以属性值作为键
def getEntProp2dict(self,question):
ent = dict()
prop = dict()
start_time = time.time()
ans = self.ner.predict(question)
if not ans:
ans = ans = self.ner.predict(question + '?')
end_time = time.time()
print('实体识别耗时：%s'%(end_time-start_time))
print('实体属性识别结果：%s'%(ans))
if ans:
for val in ans:
if val[1] == 'SENT' or val[1] == 'OENT':
ent[val[0]]=val[1]
elif val[1] == 'PROP':
pos = question.find(val[0])
prop[val[0]] = pos
return ent,prop
# 加载停用词词典
def removeStopWordsList(self,question:str):
for i in range(len(self.stopWords)):
if self.stopWords[i] in question:
question = question.replace(self.stopWords[i],'')
return question
# 属性映射计算相似度
def getSimilarityAttr(self,):
with open(self.attrFile,'r',encoding='utf-8') as f:
for line in f.readlines():
line = line.replace('\n','')
line_list = line.split(' ')
for i in line_list:
for j in line_list:
if i != j:
self.attr_dict[i].append(j)
# print(self.attr_dict)
return self.attr_dict
# elasticsearch模糊查询
def getSimEntFromElastic(self,question):
es_url = os.getenv('NEO4J_BASE')
if es_url =='http://keylab.jios.org:7474':
es_url = "http://localhost:9200/"
else:
es_url = "http://192.168.0.55:9200/"
ent,prop = self.getEntProp2dict(question)
ent_list = list(ent.keys())
ents = ent_list[0]
self.loadFuzzyDict(self.fuzzyFile)
if ents in self.fuzzy_dict:
entity = self.fuzzy_dict[ents]
question = question.replace(ents,entity)
return entity,question
self.query = json.dumps({"self.query": { "bool":{"must":[{"match":{"Entity":ents}}]}},"from":0,"size":10},ensure_ascii=False)
self.query = self.query.encode('utf-8')
url_01 = es_url + 'node_实体' + '/' + 'Entity' + '/_search'
start_time = time.time()
response = requests.get(url_01,headers={"Content-Type":"application/json"}, data = self.query)
end_time = time.time()
# print('ES查询耗时：%s'%(end_time-start_time))
res = json.loads(response.content)
if res['hits']['hits']:
ans = res['hits']['hits'][0]['_source']['Entity']
question = question.replace(ents,ans)
return ans,question
else:
return 'none',question
def loadFuzzyDict(self,file):
with open(file,'r',encoding='utf-8') as f:
for line in f.readlines():
line = line.replace('\n','')
line_list = line.split(':')
self.fuzzy_dict[line_list[0]]=line_list[1]
class MergeProperty(object):
def __init__(self,):
self.dict = {}
def mergeProperty(self,dataDict:list):
res_dict = dict()
if not dataDict:
return res_dict
for qiyiDict in dataDict:
entity = qiyiDict['n.name']
weight = qiyiDict['weight']
r2_name = qiyiDict['r2.name']
n2_name = qiyiDict['n2.name']
temp_dict =dict()
temp_list = []
if entity not in res_dict:
temp_dict['weight'] = [weight]
temp_dict[r2_name] = [n2_name]
res_dict[entity]=temp_dict
else:
if r2_name in res_dict[entity]:
temp_list = res_dict[entity][r2_name]
if n2_name not in temp_list:
temp_list.append(n2_name)
res_dict[entity][r2_name] = temp_list
else:
temp_list.append(n2_name)
res_dict[entity][r2_name] = temp_list
return res_dict
def merge_S_P_Property(self,spDict:list):
res_dict = dict()
if not spDict:
return res_dict
for p_dict in spDict:
r_name = p_dict['r.name']
m_name = p_dict['m.name']
if r_name not in res_dict:
res_dict[r_name]=[m_name]
else:
res_dict[r_name] = [m_name] + res_dict[r_name]
return res_dict
class GiveFlaskWebData(object):
def __init__(self,):
self.qa = AnswerByOwnthink()
self.query = Query()
def getWebTypeData(self, question: str):
data = []
link = []
start_time = time.time()
ans, searchPath, sim, question_type = self.qa.searchQustion(question)
end_time = time.time()
timeUsed = str(int(float('%.3f' % (end_time - start_time)) * 1000)) + 'ms'
ques_type_list = ['SP->O','SPP->O','SO->P','PO->S','OP->S']
question_type = ques_type_list[int(question_type)]
answer = {
'查询路径': question_type,
'返回答案': ans,
'答案得分': sim,
'用时':timeUsed
}
if ans == '未识别到实体，请重新输入您的问题' or ans == '识别到问题类型未SO->P，但实体数目低于或超过了2个，请重新输入。':
return answer, data, link
elif ans == '实体无关系词，查询结果：无':
res,que = self.qa.getSimEntFromElastic(question)
if res != 'none':
ans = '您输入的问句中实体不存在，您是否想查询：%s'%(que)
answer = {
'查询路径': question_type,
'返回答案': ans,
'答案得分': sim,
'用时':timeUsed
}
return answer, data, link
else:
return answer, data, link
if question_type == 'SP->O':
ent_attr_list = searchPath.split('->')
res_list = self.query.S_P(ent_attr_list[0])
idx = 1
temp_dict = {
# 'id':idx,
'name': ent_attr_list[0],
'symbolSize': 90,
'category': 0
}
data.append(temp_dict)
r_name_list = []
m_name_list = [ent_attr_list[0]]
for ans_dict in res_list:
r_name = ans_dict['r.name']
m_name = ans_dict['m.name']
if r_name not in r_name_list and len(r_name_list) <= 20 and m_name not in m_name_list:
r_name_list.append(r_name)
idx += 1
m_name = m_name.replace('\n',',')
temp_dict = {
# 'id':idx,
'name': m_name,
'symbolSize': 75,
'category': 1
}
if m_name in ans:
temp_dict['category'] = 2
temp_dict['name'] = ans
m_name = ans
if temp_dict not in data:
data.append(temp_dict)
link_dict = {
'source': ent_attr_list[0],
'target': m_name,
'name': r_name
}
if link_dict not in link:
link.append(link_dict)
temp_dict = {
# 'id':idx,
'name': ans,
'symbolSize': 75,
'category': 2
}
if temp_dict not in data:
data.append(temp_dict)
link_dict = {
'source': ent_attr_list[0],
'target': ans,
'name': ent_attr_list[1]
}
if link_dict not in link:
link.append(link_dict)
return answer, data, link
elif question_type == 'SPP->O':
ent_attr_list = searchPath.split('->')
ent_o1 = ent_attr_list[2]
res_list = self.query.S_P(ent_attr_list[0])
temp_dict = {
'name': ent_attr_list[0],
'symbolSize': 90,
'category': 0
}
data.append(temp_dict)
r_name_list = []
for ans_dict in res_list:
r_name = ans_dict['r.name']
m_name = ans_dict['m.name']
if r_name not in r_name_list and len(r_name_list) <=20 and m_name != ans:
r_name_list.append(r_name)
m_name = m_name.replace('\n',',')
temp_dict = {
'name': m_name,
'symbolSize': 75,
'category': 1
}
if m_name == ent_o1:
temp_dict['category'] = 2
if temp_dict not in data :
data.append(temp_dict)
link_dict = {
'source': ent_attr_list[0],
'target': m_name,
'name': r_name
}
if link_dict not in link:
link.append(link_dict)
temp_dict = {
'name':ans,
'symbolSize': 90,
'category': 2
}
if temp_dict not in data:
data.append(temp_dict)
link_dict = {
'source': ent_attr_list[2],
'target': ans,
'name': ent_attr_list[-1]
}
if link_dict not in link:
link.append(link_dict)
return answer, data, link
elif question_type == 'PO->S' or question_type == 'OP->S':
ent_attr_list = searchPath.split('->')
res_list = self.query.OP_S_ByEntity(ent_attr_list[-1])
temp_dict = {
'name': ent_attr_list[-1],
'symbolSize': 90,
'category': 0
}
data.append(temp_dict)
r_name_list = []
for ans_dict in res_list:
r_name = ans_dict['r.name']
m_name = ans_dict['m.name']
if r_name not in r_name_list and len(r_name_list) <=20 and m_name != ent_attr_list[-1]:
r_name_list.append(r_name)
m_name = m_name.replace('\n',',')
temp_dict = {
'name': m_name,
'symbolSize': 75,
'category': 1
}
if m_name in ans:
temp_dict['category'] = 2
temp_dict['name'] = ans
m_name = ans
if temp_dict not in data:
data.append(temp_dict)
link_dict = {
'source': m_name,
'target': ent_attr_list[-1],
'name':r_name
}
if link_dict not in link:
link.append(link_dict)
return answer, data, link
elif question_type == 'SO->P':
if '-[r]-' in searchPath:
ent_attr_list = searchPath.split('-[r]-')
res_list = self.query.S_P(ent_attr_list[0])
temp_dict = {
'name': ent_attr_list[0],
'symbolSize': 90,
'category': 0
}
data.append(temp_dict)
r_name_list = []
for ans_dict in res_list:
r_name = ans_dict['r.name']
m_name = ans_dict['m.name']
if r_name not in r_name_list and len(r_name_list) <=20:
r_name_list.append(r_name)
temp_dict = {
'name': m_name,
'symbolSize': 75,
'category': 1
}
if m_name == ent_attr_list[-1]:
temp_dict['category'] = 2
if temp_dict not in data:
data.append(temp_dict)
link_dict = {
'source': ent_attr_list[0],
'target': m_name,
'name': r_name
}
if link_dict not in link:
link.append(link_dict)
ans_spo_list = ans.split(':')
temp_dict = {
'name': ent_attr_list[-1],
'symbolSize': 75,
'category': 2
}
link_dict = {
'source': ent_attr_list[0],
'target': ent_attr_list[-1],
'name': ans_spo_list[1]
}
if temp_dict not in data:
data.append(temp_dict)
if link_dict not in link:
link.append(link_dict)
return answer,data,link
else:
ent_attr_list = searchPath.split('-')
temp_dict = {
'name': ent_attr_list[0],
'symbolSize': 70,
'category': 0
}
data.append(temp_dict)
temp_dict = {
'name': ent_attr_list[-1],
'symbolSize': 70,
'category': 0
}
data.append(temp_dict)
searchPath = searchPath.replace(ent_attr_list[0],'').replace(ent_attr_list[-1],'')
link_dict = {
'source': ent_attr_list[0],
'target': ent_attr_list[-1],
'name': searchPath
}
link.append(link_dict)
return answer,data,link